Applied Soft Computing最新文献_第5页

Comparative analysis of deep learning algorithms for predicting construction project delays in Saudi Arabia

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-18 DOI: 10.1016/j.asoc.2025.112890

Saleh Alsulamy

{"title":"Comparative analysis of deep learning algorithms for predicting construction project delays in Saudi Arabia","authors":"Saleh Alsulamy","doi":"10.1016/j.asoc.2025.112890","DOIUrl":"10.1016/j.asoc.2025.112890","url":null,"abstract":"<div><div>Construction projects in Saudi Arabia often encounter delays, which present significant challenges to project managers and result in financial losses and stakeholder dissatisfaction. Effectively managing these delays is essential for maintaining project timelines and optimizing resource use. This study explores the hypothesis that advanced deep learning algorithms can significantly improve the prediction and management of construction project delays in Saudi Arabia. The research focuses on three algorithms: Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP), evaluating their effectiveness across datasets with varying class imbalances. A structured methodology was employed to assess the algorithms based on key performance metrics, including accuracy, precision, sensitivity, specificity, and misclassification errors. GAN, LSTM, and MLP were trained and tested using real-world construction project data, incorporating tools such as k-fold cross-validation for validation. The GAN model achieved the highest accuracy at 91 %, with a misclassification error of 9 %, outperforming both LSTM (accuracy: 88 %, error: 12 %) and MLP (accuracy: 83 %, error: 17 %). GAN also demonstrated superior precision (90 %) and sensitivity (87 %), making it the most reliable algorithm for delay risk assessment. While LSTM was effective, it had slightly lower precision (88 %) but exhibited strong generalization to unseen data. MLP showed the weakest performance, with higher misclassification rates and lower robustness. These findings suggest that deep learning models, particularly GAN, can significantly improve decision-making and delay mitigation in construction projects.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112890"},"PeriodicalIF":7.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient fuzzy-based high utility pattern computing and analyzing approach with temporal properties

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-18 DOI: 10.1016/j.asoc.2025.112902

Unil Yun, Hyeonmo Kim , Hanju Kim , Seungwan Park

{"title":"Efficient fuzzy-based high utility pattern computing and analyzing approach with temporal properties","authors":"Unil Yun, Hyeonmo Kim , Hanju Kim , Seungwan Park","doi":"10.1016/j.asoc.2025.112902","DOIUrl":"10.1016/j.asoc.2025.112902","url":null,"abstract":"<div><div>Fuzzy logic in soft computing deals with intuitive and comprehensive intelligence to find solutions to problems in the uncertain real world. Considering the fuzzy set concept and knowledge discovery of utility-driven patterns simultaneously, quantities of sets of items hidden within vast data can be represented in an easy-to-understand linguistic representation. This can lead to more reasonable decision-making. Together with these fascinating results, temporal fuzzy utility pattern analysis has emerged as a significant area in the last few years to consider the duration of transactions in temporal quantitative data. The latest temporal approaches have improved resource efficiency by storing information on patterns with efficient data structures. However, although a list-based approach is known to be robust and follows a mechanism that does not generate candidates, it requires explosive comparison operations that are unsuitable for processing long-length patterns, especially in big data analysis. To solve this issue, we present a novel indexed list-based structure along with a data analysis method designed to allow rapid pattern growth as well as prevent the generation of candidates for discovering high temporal fuzzy utility patterns. Performance tests on real and synthetic datasets demonstrate that the proposed approach exhibits superior time efficiency and scalability relative to state-of-the-art methods with minimal compromise in memory, all while extracting accurate results. Moreover, comprehensive experiments demonstrate the capability of the proposed method for practical use cases and its effectiveness in search space pruning.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112902"},"PeriodicalIF":7.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning efficient branch-and-bound for solving Mixed Integer Linear Programs 学习解决混合整型线性规划的高效分支与边界

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112863

Shuhan Du, Junbo Tong, Wenhui Fan

{"title":"Learning efficient branch-and-bound for solving Mixed Integer Linear Programs","authors":"Shuhan Du, Junbo Tong, Wenhui Fan","doi":"10.1016/j.asoc.2025.112863","DOIUrl":"10.1016/j.asoc.2025.112863","url":null,"abstract":"<div><div>Mixed Integer Linear Programs (MILPs) are widely used to model various real-world optimization problems, traditionally solved using the branch-and-bound (B&B) algorithm framework. Recent advances in Machine Learning (ML) have inspired enhancements in B&B by enabling data-driven decision-making. Two critical decisions in B&B are node selection and variable selection, which directly influence computational efficiency. While prior studies have applied ML to enhance these decisions, they have predominantly focused on either node selection or variable selection, addressing the decision individually and overlooking the significant interdependence between the two. This paper introduces a novel ML-based approach that integrates both decisions within the B&B framework using a unified neural network architecture. By leveraging a bipartite graph representation of MILPs and employing Graph Neural Networks, the model learns adaptive strategies tailored to different problem types through imitation of expert-designed policies. Experiments on various benchmarks show that the integrated policy adapts better to different problem classes than models targeting individual decisions, delivering strong performance in solving time, search tree size, and optimization dynamics across various configurations. It also surpasses competitive baselines, including the state-of-the-art open-source solver SCIP and a recent reinforcement learning-based approach, demonstrating its potential for broader application in MILP solving.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112863"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Adaptation via Feature Disentanglement for cross-domain image classification

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112868

Zhi-Ze Wu , Chang-Jiang Du , Xin-Qi Wang , Le Zou , Fan Cheng , Teng Li , Fu-Dong Nian , Thomas Weise , Xiao-Feng Wang

{"title":"Domain Adaptation via Feature Disentanglement for cross-domain image classification","authors":"Zhi-Ze Wu , Chang-Jiang Du , Xin-Qi Wang , Le Zou , Fan Cheng , Teng Li , Fu-Dong Nian , Thomas Weise , Xiao-Feng Wang","doi":"10.1016/j.asoc.2025.112868","DOIUrl":"10.1016/j.asoc.2025.112868","url":null,"abstract":"<div><div>Image classification is an important application area of soft computing. In many real-world application scenarios, image classifiers are applied to domains that differ from the original training data. This so-called domain shift significantly reduces classification accuracy. To tackle this issue, unsupervised domain adaptation (UDA) techniques have been developed to bridge the gap between source and target domains. These techniques achieve this by transferring knowledge from a labeled source domain to an unlabeled target domain. We develop a novel and effective coarse-to-fine domain adaptation method called Domain Adaptation via Feature Disentanglement (DAFD), which has two new key components: First, our Class-Relevant Feature Selection (CRFS) module disentangles class-relevant features from class-irrelevant ones. This prevents the network from overfitting to irrelevant data and enhances its focus on crucial information for accurate classification. This reduces the complexity of domain alignment, which improves the classification accuracy on the target domain. Second, our Dynamic Local Maximum Mean Discrepancy module DLMMD achieves a fine-grained feature alignment by minimizing the discrepancy among class-relevant features from different domains. The alignment process now becomes more adaptive and contextually sensitive, enhancing the ability of the model to recognize domain-specific patterns and characteristics. The combination of the CRFS and DLMMD modules results in an effective alignment of class-relevant features. Domain knowledge is successfully transferred from the source to the target domain. Our comprehensive experiments on four standard datasets demonstrate that DAFD is robust and highly effective in cross-domain image classification tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112868"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving an imperfect EPQ model with safety stock for type-I and type-II screening error under constrained fuzzy Newton interpolation approach

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112866

Mou Jana , Sujit Kumar De , Adrijit Goswami

{"title":"Solving an imperfect EPQ model with safety stock for type-I and type-II screening error under constrained fuzzy Newton interpolation approach","authors":"Mou Jana , Sujit Kumar De , Adrijit Goswami","doi":"10.1016/j.asoc.2025.112866","DOIUrl":"10.1016/j.asoc.2025.112866","url":null,"abstract":"<div><div>This article deals with an industrial production process of a single item with safety stock and deterioration over time. First of all, we have considered an economic production quantity (EPQ) inventory model where the items are screened multiple times and the screening process itself has Type-I and Type-II errors. Some parts of the imperfect items are reworkable (serviceable) and the unusable items are discarded from the inventory instantly. Incorporating rework cost, disposal cost and screening cost in the inventory process, a total average system cost function has been studied and it has been optimized analytically. But to capture the flexibilities of the demand rate and all unit cost components (for comparative analysis) a fuzzy model has been developed. Indeed, we know that the defuzzification is a crucial step in any fuzzy inferential system, aimed at converting fuzzy outputs into equivalent crisp values for final decision-making. To get the model optimum we optimize the fuzzy membership function developed with the help of Newton’s general interpolation formula for the proposed constrained non-linear optimization problem. The major novelties of this work include the construction of a new fuzzy membership function and techniques of decision making by means of a solution algorithm. For model validation, a numerical example has been analyzed on the basis of a case study and it has been compared with some of the existing methods. Findings reveal that the proposed method dominates others and up to 39.36% cost reduction is possible as a whole. Finally, sensitivity analysis and graphical illustrations have been carried out, followed by scope of future work.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112866"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parallel attribute reduction in high-dimensional data: An efficient MapReduce strategy with fuzzy discernibility matrix

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112870

Pandu Sowkuntla , P.S.V.S. Sai Prasad

{"title":"Parallel attribute reduction in high-dimensional data: An efficient MapReduce strategy with fuzzy discernibility matrix","authors":"Pandu Sowkuntla , P.S.V.S. Sai Prasad","doi":"10.1016/j.asoc.2025.112870","DOIUrl":"10.1016/j.asoc.2025.112870","url":null,"abstract":"<div><div>The hybrid paradigm of fuzzy-rough set theory, which combines fuzzy and rough sets, has proven effective in attribute reduction for hybrid decision systems encompassing both numerical and categorical attributes. However, current parallel/distributed approaches are limited to handling datasets with either categorical or numerical attributes and often rely on fuzzy dependency measures. There exists little research on parallel/distributed attribute reduction for large-scale hybrid decision systems. The challenge of handling high-dimensional data in hybrid decision systems necessitates efficient distributed computing techniques to ensure scalability and performance. MapReduce, a widely used framework for distributed processing, provides an organized approach to handling large-scale data. Despite its potential, there is a noticeable lack of attribute reduction techniques that leverage MapReduce’s capabilities with a fuzzy discernibility matrix, which can significantly improve the efficiency of processing high-dimensional hybrid datasets. This paper introduces a vertically partitioned fuzzy discernibility matrix within the MapReduce computation model to address the high dimensionality of hybrid datasets. The proposed MapReduce strategy for attribute reduction minimizes data movement during the shuffle and sort phase, overcoming limitations present in existing approaches. Furthermore, the method’s efficiency is enhanced by integrating a feature known as SAT-region removal, which removes matrix entries that satisfy the maximum satisfiability conditions during the attribute reduction process. Extensive experimental analysis validates the proposed method, demonstrating its superior performance compared to recent parallel/distributed methods in attribute reduction.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112870"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven evolutionary algorithms based on initialization selection strategies, POX crossover and multi-point random mutation for flexible job shop scheduling problems

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112901

Ruxin Zhao , Lixiang Fu , Jiajie Kang , Chang Liu , Wei Wang , Haizhou Wu , Yang Shi , Chao Jiang , Rui Wang

{"title":"Data-driven evolutionary algorithms based on initialization selection strategies, POX crossover and multi-point random mutation for flexible job shop scheduling problems","authors":"Ruxin Zhao , Lixiang Fu , Jiajie Kang , Chang Liu , Wei Wang , Haizhou Wu , Yang Shi , Chao Jiang , Rui Wang","doi":"10.1016/j.asoc.2025.112901","DOIUrl":"10.1016/j.asoc.2025.112901","url":null,"abstract":"<div><div>In the fields of manufacturing and production, the precise solution of the flexible job shop scheduling problem (FJSP) is crucial for improving production efficiency and optimizing resource allocation. However, the complexity of FJSP often leads traditional optimization methods to face high computational costs and lengthy processing times. To address this problem, we propose a data-driven evolutionary algorithm based on initialization selection strategies, POX crossover, and multi-point random mutation (DDEA-PMI). This algorithm replaces the real objective function by constructing a radial basis function (RBF) surrogate model to reduce expensive computational costs and shorten solution time. In the process of solving FJSP, we use global selection (GS), local selection (LS), and random selection (RS) initialization selection strategies to obtain an initial population with high diversity. In order to reduce the generation of infeasible solutions, we use the POX crossover operator, which selects partial gene sequences from the parent generation and maps them to the offspring to preserve excellent features and ensure the feasibility of the solution. In addition, we design a multi-point random mutation operation to enhance the diversity of the population. Through the multi-point mutation strategy, it is able to explore more comprehensively in the solution space to increase the possibility of finding the optimal solution. To verify the effectiveness of DDEA-PMI, we compare it with three same types of data-driven evolutionary algorithms. We compare and analyze the DDEA-PMI with three algorithms after removing one of our proposed strategies. The experimental results show that DDEA-PMI is effective and has advantages in solving FJSP.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112901"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On generalized Sugeno’s class generator and parametrized intuitionistic fuzzy approach for enhancing low-light images

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112865

Maheshkumar C.V. , David Raj M. , Saraswathi D.

{"title":"On generalized Sugeno’s class generator and parametrized intuitionistic fuzzy approach for enhancing low-light images","authors":"Maheshkumar C.V. , David Raj M. , Saraswathi D.","doi":"10.1016/j.asoc.2025.112865","DOIUrl":"10.1016/j.asoc.2025.112865","url":null,"abstract":"<div><div>Enhancing low-light images poses a significant challenge in terms of pixel distortion, color degradation, detail loss, over enhancement and noise amplification, particularly in images that have both low light and normal light region. In recent years, researchers have increasingly turned their attention to intuitionistic fuzzy set based approaches for low light image enhancement due to their flexibility in the representation of a pixel. In this work, the generalized Sugeno’s class of generating function is proposed. Since the parameter value in the existing generating functions lies in an unbounded interval, it is difficult to find the best parameter value. By using the proposed generalized version, a few intuitionistic generating functions are analyzed where the parameter value lies in a bounded interval. A searching algorithm is also proposed to find the parameter value that maximizes the entropy of an image for any membership and generating function. Regardless of the number of decimals, the proposed approach finds the best parameter value iteratively. Then, in HSI color space, an enhancement model is designed utilizing the intuitionistic fuzzy image achieved using best parameter value and contrast-limited adaptive histogram equalization. The proposed method performs better compared to the state-of-the-art models. Also, seven image quality mathematical metrics — entropy, SSIM, correlation coefficient <span><math><mrow><mo>(</mo><mi>r</mi><mo>)</mo></mrow></math></span>, PSNR, AMBE, number of edge pixels <span><math><mrow><mo>(</mo><msub><mrow><mi>N</mi></mrow><mrow><mi>g</mi></mrow></msub><mo>)</mo></mrow></math></span> and the fitness function are implemented to compare the proposed and state-of-the-art models.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112865"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A directed batch growing self-organizing map based niching differential evolution for multimodal optimization problems

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112862

Mahesh Shankar , Palaniappan Ramu , Kalyanmoy Deb

{"title":"A directed batch growing self-organizing map based niching differential evolution for multimodal optimization problems","authors":"Mahesh Shankar , Palaniappan Ramu , Kalyanmoy Deb","doi":"10.1016/j.asoc.2025.112862","DOIUrl":"10.1016/j.asoc.2025.112862","url":null,"abstract":"<div><div>Many real-world optimization problems naturally result in multiple optimal solutions, thereby falling in the class of multimodal optimization problems (MMOPs). A task of finding a plurality of optimal solutions for MMOPs comes under the scope of multimodal optimization algorithms (MMOAs). To solve MMOPs, <em>niching</em> techniques are usually employed by proactively modifying standard evolutionary algorithms (EAs) to form stable subpopulations around multiple niches within their evolving populations. This way, each optimum can germinate and eventually help form a cloud of solutions around each optimum parallely, thereby finding multiple (but a finite number of) optima simultaneously. However, several existing niching techniques suffer from common drawbacks, such as sensitivity with niching parameters or poor performance on high-dimensional problems. An efficient niching technique needs an effective population partitioning method around distinct leading solutions representing each optimum. In this paper, we propose a directed batch growing self-organizing map based niching differential evolution (DBGSOM-NDE). For this purpose, a standard differential evolution (DE) method is divided into two overlapping phases: (i) population-wide search (PS) and (ii) niche-wide search (NS). PS executes neighborhood search around each individual, promoting exploration, while NS explores only the leaders, thus reducing the effect of exploration for a better search intensification around the leaders using a Cauchy-distribution based local search to improve them. We evaluate the role of each operator of the proposed approach DBGSOM-NDE and compare its performance with a number of state-of-the-art niching techniques demonstrating its competitiveness and superiority, especially on high-dimensional and nonlinear problems taken from the existing literature. Finally, a hyper-parametric study is provided demonstrating weak dependence of them to the algorithm’s performance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112862"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Space-depth mutual compensation for fine-grained fabric defect detection model

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-02-17 DOI: 10.1016/j.asoc.2025.112869

Kailong Zhou, Jianhui Jia, Weitao Wu, Miao Qian, Zhong Xiang

{"title":"Space-depth mutual compensation for fine-grained fabric defect detection model","authors":"Kailong Zhou, Jianhui Jia, Weitao Wu, Miao Qian, Zhong Xiang","doi":"10.1016/j.asoc.2025.112869","DOIUrl":"10.1016/j.asoc.2025.112869","url":null,"abstract":"<div><div>In recent years, using the deep learning approach in the textile industry for defect detection has emerged as a prominent research. However, detecting fabric defects remains challenging due to the small size and small number of fabric defect features. Traditional down-sampling operations that result in loss of feature information, interpolation up-sampling operations that add a lot of background redundant information, and interference with fabric images from external sources such as lighting or electromagnetic devices are significant barriers to achieving accurate defect detection using existing methods. In this work, we introduced a lightweight fabric defect detection method with enhanced resistance to interference. Firstly, we use YOLOv7-tiny as the basic model and integrate the Spatial Pyramid Dilated Convolution (SPD) and Efficient Channel Attention (ECA) modules to enhance the original MP-1 and Effective Long-Range Aggregation Network (ELAN) modules to retain fine-grained information, solve the problem of down-sampled feature loss and improve feature importance allocation. Secondly, a distinctive up-sampling Module (DTS) was proposed to replace the traditional interpolation up-sampling. The module expands the feature map size without adding extraneous information, thus ensuring more efficient integration of features of different sizes. Finally, a novel noise filtering technique called the Color Space Iterative (CSI) method was proposed to filter noise interference quickly and conveniently. Experiments on the open-source DAGM and TILDA defect datasets, as well as supplementary tests on CIFAR10 datasets for the CSI method, have yielded promising results. With a mere 3.4M parameters, the proposed lightweight model underscores the method’s superiority over the baseline in balancing model parameters, detection speed, and accuracy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"172 ","pages":"Article 112869"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0