AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

Chinese License Plate Recognition Based on OpenCV and LPCR Net 基于 OpenCV 和 LPCR Net 的中文车牌识别

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700688

Yuehua Li, Yueyue Zhang, Jinfeng Wang, Fanfan Zhong, Bin Hu

{"title":"Chinese License Plate Recognition Based on OpenCV and LPCR Net","authors":"Yuehua Li, Yueyue Zhang, Jinfeng Wang, Fanfan Zhong, Bin Hu","doi":"10.3103/S0146411624700688","DOIUrl":"10.3103/S0146411624700688","url":null,"abstract":"Aiming to solve the low accuracy and slow speed of Chinese character recognition in the traditional license plate recognition, a method of license plate location, character segmentation and recognition using computer vision library OpenCV and license plate character recognition convolutional neural network (LPCR Net) is proposed. First, the RGB three-channel image is separated from the input image, and the input image is binarized by calculating the color characteristics of the license plate, then the multiple connected regions are obtained through morphological operations such as expansion and closure, the license plate location is completed via calculating the standard license plate aspect ratio and area; secondly, the horizontal and vertical projection method used in the traditional license plate character segmentation is improved to complete the license plate character segmentation, which improves the accuracy and speed of Chinese character segmentation; finally, the license plate character recognition is completed based on LPCR Net, and the recognition accuracy rate reaches 98.33%, which is 3.11% higher than that of AlexNet. Experimental results show that the proposed method can effectively improve the accuracy of license plate location, character segmentation and recognition.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"580 - 591"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Groundwater Level Prediction Method in Karst Areas Based on Improved Attention Mechanism Fusion Time Convolutional Network 基于改进注意机制融合时间卷积网络的岩溶地区地下水位预测方法研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700603

Lina Yu, Yinjun Zhou, Yao Hu

引用次数: 0

Road Traffic Classification from Nighttime Videos Using the Multihead Self-Attention Vision Transformer Model and the SVM 使用多头自注意视觉变换器模型和 SVM 从夜间视频中进行道路交通分类

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700652

Sofiane Abdelkrim Khalladi, Asmâa Ouessai, Mokhtar Keche

{"title":"Road Traffic Classification from Nighttime Videos Using the Multihead Self-Attention Vision Transformer Model and the SVM","authors":"Sofiane Abdelkrim Khalladi, Asmâa Ouessai, Mokhtar Keche","doi":"10.3103/S0146411624700652","DOIUrl":"10.3103/S0146411624700652","url":null,"abstract":"Intelligent transport systems (ITSs) have emerged as a groundbreaking solution to address the challenges associated with road traffic, which are enhancing road utilization efficiency, providing convenient and safe transportation, and reducing energy consumption. ITS leverages advanced technologies to collect, store, and deliver real-time road traffic information, enabling intelligent decision-making and optimizing various aspects of transportation systems. As a contribution in this matter, we propose in this paper a novel efficient macroscopic approach, based on the multihead self-attention vision transformer (MSViT), for categorizing road traffic congestion, from nighttime videos, into three classes: light, medium, and heavy. To assess the performance of our approach, we conducted experiments using the nighttime UCSD (University of California San Diego) dataset, which includes various weather conditions (clear, overcast, and rainy) and traffic scenarios (light, medium, and heavy). The classification accuracy reached a high level of 94.24%. By incorporating a support vector machine (SVM) classifier into this method, we managed to enhance this accuracy to the outstanding level of 98.92%, thus outperforming the existing state-of-the-art methods that were evaluated using the same UCSD dataset, furthermore, the execution time was optimized.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"544 - 554"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insulator Defect Detection of Lightweight Rotating YOLOv5 Based on Adaptive Feature Fusion 基于自适应特征融合的轻型旋转 YOLOv5 绝缘子缺陷检测

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700640

Jiang Xiang Ju, Wang Rui Tong

{"title":"Insulator Defect Detection of Lightweight Rotating YOLOv5 Based on Adaptive Feature Fusion","authors":"Jiang Xiang Ju,  Wang Rui Tong","doi":"10.3103/S0146411624700640","DOIUrl":"10.3103/S0146411624700640","url":null,"abstract":"With the construction of smart grid, aerial insulator defect detection based on computer vision has become an important task to ensure grid safety. When the target detection model is too large, it is not conducive to the edge deployment of aerial inspection UAV; Moreover, different aerial photography angles and distances will cause the insulator string in the image to have any direction and less defect information. In order to solve these problems, this paper proposes a rotating GBS-AFP-YOLOv5 model with the combination of lightweight and adaptive features. Firstly, an improved YOLOv5 based on lightweight GBS is proposed by Ghost convolution, which can effectively extract features while reducing the complexity of the model. Then, an adaptive information interaction feature pyramid (AFP) is proposed by combining CARAFE upsampling operator and ECA attention, which effectively fuses the feature information of shallow and deep defects and improves the model performance. Then, a more accurate insulator string detection method is realized by using rotating frame combined with ring label smoothing technology. Finally, the normalized wasserstein distance (NWD) is introduced to improve the loss function, which further enhances the detection ability of the model for small targets with defects. Based on the insulator data set, the test results show that the model has a good defect detection performance, which is improved from mAP0.5 to 0.923 on the basis of only 4.32M parameters.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"530 - 543"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting Algorithms 使用基于内容、协作过滤、监督学习和提升算法的集合学习混合推荐系统

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700615

Kulvinder Singh, Sanjeev Dhawan, Nisha Bali

{"title":"An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting Algorithms","authors":"Kulvinder Singh, Sanjeev Dhawan, Nisha Bali","doi":"10.3103/S0146411624700615","DOIUrl":"10.3103/S0146411624700615","url":null,"abstract":"The evolution of recommendation systems has revolutionized user experiences by providing personalized recommendations. Although conventional systems such as collaborative and content-based filtering are reliable, they still suffer from inherent limitations. We introduce a hybrid recommendation system that combines content-based filtering using TF-IDF and cosine similarity with collaborative filtering and SVD to address these challenges. We bolster our model through supervised machine learning algorithms like decision trees (DT), random forests (RF), and support vector regression (SVR). To amplify predictive prowess, boosting algorithms including CatBoost and XGBoost are harnessed. Our experiments are performed on the benchmark dataset MovieLens 1M, which highlights the superiority of our hybrid method over more traditional alternatives with SVR being the best-performing algorithm consistently. Our hybrid model achieved an MSLE score of 2.3 and an RMSLE score of 1.5, making SVR consistently the best-performing algorithm in the recommendation system. This combination demonstrates the potential of collaborative-content hybrids supported by cutting-edge machine-learning techniques to reshape the field of recommendation systems.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"491 - 505"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extraction of Features of Regular Surfaces from the Laser Point Clouds for 3D Objects 从激光点云中提取三维物体的规则表面特征

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700627

Xiaoxiao Cheng, Jianjun Wang, Jiongyu Wang, Kun Wang, Xudong Li

{"title":"Extraction of Features of Regular Surfaces from the Laser Point Clouds for 3D Objects","authors":"Xiaoxiao Cheng, Jianjun Wang, Jiongyu Wang, Kun Wang, Xudong Li","doi":"10.3103/S0146411624700627","DOIUrl":"10.3103/S0146411624700627","url":null,"abstract":"A fusion optimization algorithm has been proposed to enhance the reliability and accuracy of regular surface feature extraction from laser point clouds. to get optimal result. Firstly, the Octree-based constrained adaptive growth method is utilized to optimize the neighborhood points of point cloud and establish its topological relationship. Secondly, the Harris-3D algorithm is applied to extract key points from the point cloud data, followed by a region growth method that combines double thresholds of normal vector angle and Euclidean distance, to segment the point cloud into separate clusters. Finally, regular surface features are extracted from these clusters, allowing for the recognition of 3D object surface morphology and features. Experiments on regular surface feature extraction from point clouds have shown that the proposed fusion optimization algorithm can significantly improve the accuracy and efficiency of feature extraction. The RMS errors for the extraction and reconstruction of quadric surfaces like planes, cylinders, cones, and spheres are below 0.020 mm. Additionally, a real-world experiment involving a large amount of complex point cloud data from an unmanned laser scanning scene also confirms the effectiveness of the proposed feature extraction optimization algorithm for regular surface feature extraction, object recognition, and 3D reconstruction.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"506 - 518"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Airborne Chemical Detection Using IoT and Machine Learning in the Agricultural Area 在农业领域利用物联网和机器学习进行空中化学物质检测

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700676

Anju Augustin, Cinu C. Kiliroor

引用次数: 0

Financial Digital Images Compression Method Based on Discrete Cosine Transform 基于离散余弦变换的金融数字图像压缩方法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S014641162470069X

Wenjin Wang, Miaomiao Lu, Xuanling Dai, Ping Jiang

{"title":"Financial Digital Images Compression Method Based on Discrete Cosine Transform","authors":"Wenjin Wang, Miaomiao Lu, Xuanling Dai, Ping Jiang","doi":"10.3103/S014641162470069X","DOIUrl":"10.3103/S014641162470069X","url":null,"abstract":"In response to the characteristics of financial image data, this paper proposes an efficient digital image compression scheme. Firstly, discrete cosine transform (DCT) is applied to divide the financial image into DC and AC coefficients. Secondly, based on the characteristics of DCT coefficients, a fuzzy method is employed to categorize DCT subblocks into smooth, texture, and edge classes, enabling distinct quantization strategies. Subsequently, to eliminate spatial and statistical redundancies in financial images, common features and structures are utilized, and a specific scanning approach is employed to optimize the arrangement of important coefficients. Finally, differential prediction and entropy coding are employed for DCT coefficient scanning encoding, enhancing compression efficiency. The objective evaluation metrics of this algorithm are approximately 2 dB higher than existing algorithms at bit rates of 0.25 and 0.5. Even at bit rates of 0.75, 1.5, 2.5, and 3.5, the performance of this method still outperforms the comparative algorithms, demonstrating its capability to efficiently store and transmit massive financial image data, thereby providing robust support for data processing in the financial sector.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"592 - 601"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Arabic Optical Character Recognition Approach Based on Levenshtein Distance 基于莱文斯坦距离的新型阿拉伯语光学字符识别方法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700639

Walid Fakhet, Salim El Khediri, Salah Zidi

引用次数: 0

Advancing Driver Behavior Recognition: An Intelligent Approach Utilizing ResNet 推进驾驶员行为识别：利用 ResNet 的智能方法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-11-06 DOI: 10.3103/S0146411624700664

Haiyan Kang, Congming Zhang, Hongling Jiang

{"title":"Advancing Driver Behavior Recognition: An Intelligent Approach Utilizing ResNet","authors":"Haiyan Kang, Congming Zhang, Hongling Jiang","doi":"10.3103/S0146411624700664","DOIUrl":"10.3103/S0146411624700664","url":null,"abstract":"In pursuit of enhancing public safety and addressing challenges in driver behavior recognition, an intelligent recognition and detection method of driver behavior based on ResNet (IRDMDB-ResNet) is proposed. The approach aims to identify instances of distracted driving resulting from abnormal behavior. Three models (IRDMDB-1, IRDMDB-2, and IRDMDB-3) are presented to implement this method, which is adapted to a deep learning behavior recognition in driving scenarios. Firstly, this study utilizes two well-tested real datasets: Driver Drowsiness Dataset and The State Farm. These datasets undergo preprocessing to meet the input requirements of the model. Secondly, a lightweight convolutional neural network model has been designed to extract features, aiding the warning system in delivering precise information and minimizing traffic collisions to the maximum extent possible. Finally, the model is evaluated based on the confusion metrics, accuracy, precision, recall, and F1-score criterion. As a result, the IRDMDB-3 model proposed in this paper can recognize and detect driver behavior effectively and stably. And it achieves 99.79% of accuracy in the classification of distracted drivers looking elsewhere in The State Farm dataset. Similarly, the detection at Driver Drowsiness Dataset is 99.68%. This advancement represents a significant improvement in traffic safety, showcasing adaptability to diverse behaviors and remarkable recognition and detection capabilities.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"555 - 568"},"PeriodicalIF":0.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0