International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献

Multimodel ensemble-based Pneumonia x-ray image classification 基于多模型集合的肺炎 X 光图像分类

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014404

Guanglong Zheng

引用次数: 0

FBS_YOLO3 vehicle detection algorithm based on viewpoint information 基于视角信息的 FBS_YOLO3 车辆检测算法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014408

Chunbao Huo, Zengwen Chen, Zhibo Tong, Ya Zheng

{"title":"FBS_YOLO3 vehicle detection algorithm based on viewpoint information","authors":"Chunbao Huo, Zengwen Chen, Zhibo Tong, Ya Zheng","doi":"10.1117/12.3014408","DOIUrl":"https://doi.org/10.1117/12.3014408","url":null,"abstract":"The FBS_YOLO3 vehicle detection algorithm is a novel solution to the challenge of detecting vehicles in unstructured road scenarios with limited warning information. This algorithm builds upon the YOLOv3 model to deliver advanced multi-scale target detection. Firstly, FBS_YOLO3 incorporates four convolutional residual structures into the YOLOv3 backbone network to obtain deeper feature knowledge via down-sampling. Secondly, the feature fusion network is improved by implementing a PAN network structure which enhances the accuracy and robustness of viewpoint recognition through top-down and bottom-up feature fusion. Lastly, the K-means clustering fusion cross-comparison loss function is utilized to redefine the anchor frame by employing a K-means fusion cross-ratio loss function. This innovative approach solves the issue of mismatching the predetermined anchor frame size of the YOLOv3 network. Experimental results demonstrate that FBS_YOLO3 on a self-built dataset can improve mAP by 3.15% compared with the original network, while maintaining a quick detection rate of 37 fps. Moreover, FBS_YOLO3 can accurately detect vehicles, identify viewpoint information, and effectively solve the warning information insufficiency problem in unstructured road scenarios.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"17 4","pages":"129690S - 129690S-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511543","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

Detection algorithm for diabetic retinopathy based on ResNet and transfer learning 基于 ResNet 和迁移学习的糖尿病视网膜病变检测算法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014400

Weihua Wang, Li Lei

引用次数: 0

Research on dark level correction method for CMOS image sensors CMOS 图像传感器暗电平校正方法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014385

Yizhe Wang, Zhongjie Guo, Youmei Guo

引用次数: 0

Detection and recongnition of pulmonary nodules based on convolution neural network 基于卷积神经网络的肺结节检测与再识别

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014478

Qiangchao Shi, Zhibing Shu

引用次数: 0

Iterative segmentation and propagation based interactive video object segmentation 基于迭代分割和传播的交互式视频对象分割

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014487

Sihan Luo, Sizhe Yang, Xia Yuan

{"title":"Iterative segmentation and propagation based interactive video object segmentation","authors":"Sihan Luo, Sizhe Yang, Xia Yuan","doi":"10.1117/12.3014487","DOIUrl":"https://doi.org/10.1117/12.3014487","url":null,"abstract":"Interactive video object segmentation (iVOS), which aims to efficiently produce high-quality segmentation masks of the target object in a video with user interactions. Recently, numerous works are proposed to advance the task of iVOS. However, their usages on user intent are limited. First, typical modules usually try to direct generate the segmentation without any further exploration on the input interaction, which misses valuable information. Second, recent iVOS approaches also do not consider the raw interactive information. As a result, the final segmentation results will be poisoned by the erroneous information given by the previous round’s segmentation masks. To solve the aforementioned weaknesses, in this paper, an Iterative Segmentation and Propagation based iVOS method is proposed to conduct better user intent exploration, namely ISP. ISP directly models user intent into the PGI2M module and TP module. Specifically, ISP first extracts a coarse-grained segmentation mask by analyzing the user’s input. Subsequently, this mask is used as a prior to aid the PGI2M module. Secondly, ISP presents a new interaction-driven self-attention module to recall the user’s intent in the TP module. Extensive experiments on two public datasets show the superiority of ISP over existing methods.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"43 5","pages":"129691A - 129691A-10"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511338","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 object detection for small objects in agriculture: taking red bayberry as an example 农业小物体检测研究：以红杨梅为例

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014464

Shan Hua, Kaiyuan Han, Shuangwei Li, Minjie Xu, Shouyan Zhu, Zhifu Xu

引用次数: 0

Rice extraction from Sentinel-2A image based on feature optimization and UPerNet:Swin Transformer model 基于特征优化和 UPerNet:Swin Transformer 模型的哨兵-2A 图像水稻提取技术

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014406

Yu Wei, Bo Wei, Xianhua Liang, Zhiwei Qi

{"title":"Rice extraction from Sentinel-2A image based on feature optimization and UPerNet:Swin Transformer model","authors":"Yu Wei, Bo Wei, Xianhua Liang, Zhiwei Qi","doi":"10.1117/12.3014406","DOIUrl":"https://doi.org/10.1117/12.3014406","url":null,"abstract":"Starting from the problem that rice extraction from remote sensing images still faces effective feature construction and extraction model, the feature optimization and combined deep learning model are considered. Taking Sentinel-2A image as data source, a multi-dimensional feature data set including spectral features, red edge features, vegetation index, water index and texture features is constructed. The ReliefF-RFE algorithm is used to optimize the features of the data set for rice extraction, and the combined UPerNet-Swin Transformer model is used to extract the rice from the study area based on the optimized features. Comparison with other feature combination schemes and deep learning models demonstrates that: (1) using the optimized features based on the ReliefF-RFE algorithm has the best segmentation effect for rice extraction, which its accuracy, recall rate, F1 score and IoU reach 92.77%, 92.28%, 92.52% and 86.09%, respectively, and (2) compared with PSPNet, Unet, DeepLabv3+ and the original UPerNet models, the combined UPerNet-Swin Transformer model has fewer misclassifications and omissions under the same optimal feature combination schemes, which the F1 score and IoU are increased by 11.12% and 17.46%, respectively","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"15 2-4","pages":"129691L - 129691L-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511391","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 brocade defect detection algorithm based on deep learning 基于深度学习的织锦缺陷检测算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014538

Ning Yun

{"title":"Research on brocade defect detection algorithm based on deep learning","authors":"Ning Yun","doi":"10.1117/12.3014538","DOIUrl":"https://doi.org/10.1117/12.3014538","url":null,"abstract":"The brocade weaving craft has a long history, with exquisite patterns and profound cultural connotations. It is an excellent representative of Chinese silk culture and an eye-catching business card in the intangible cultural heritage of mankind. The process of making brocade is a very complicated craft. In order to be able to detect defects in time during the production process, an improved SE-SSD fabric defect detection algorithm is proposed for the low efficiency of defect detection in traditional production, the large model affects the deployment and the shortcomings of DB-YOLOv3. By improving the network structure and optimizing the prior frame adjustment mechanism, the algorithm improves the ability of model feature extraction and greatly reduces the parameters and calculation of the network. The experimental results show that the SE-SSD algorithm effectively improves the missed detection of linear and weak target defects. Compared with the SSD network, the detection accuracy is increased by 27.55%, reaching 93.08% mAP, the detection speed is increased to 49FPS, and the network parameters are reduced. 51.5%, which improves the practicability of the algorithm, and the ability to detect small target defects still needs to be improved.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"12 2","pages":"1296907 - 1296907-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511399","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 mine moving target detection method based on deep learning 基于深度学习的地雷移动目标检测方法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014398

Jiaheng Zhang, Peng Mei, Yongsheng Yang

引用次数: 0