Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision最新文献_第2页

Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge 基于边缘的交通安全目标检测模型性能评价

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582178

Anilcan Bulut, Fatmanur Ozdemir, Y. S. Bostanci, M. Soyturk

{"title":"Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on Edge","authors":"Anilcan Bulut, Fatmanur Ozdemir, Y. S. Bostanci, M. Soyturk","doi":"10.1145/3582177.3582178","DOIUrl":"https://doi.org/10.1145/3582177.3582178","url":null,"abstract":"Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the other hand, low-latency requirements and available resources present challenges. Edge computing integrated with cloud computing minimizes communication delays but requires efficient use of resources due to its limited resources. For example, although deep learning-based object detection methods give very accurate and reliable results, they require high computational power. This overhead reveals a need to implement deep learning models with less complex architectures for edge deployment. In this paper, the performance of evolving deep learning models with their lightweight versions such as YOLOv5-Nano, YOLOX-Nano, YOLOX-Tiny, YOLOv6-Nano, YOLOv6-Tiny, and YOLOv7-Tiny are evaluated on a commercially available edge device. The results show that YOLOv5-Nano and YOLOv6-Nano with their TensorRT versions can provide real-time applicability in approximately 35 milliseconds of inference time. It is also observed that YOLOv6-Tiny gives the highest average precision while YOLOv5-Nano gives the lowest energy consumption when compared to other models.","PeriodicalId":170327,"journal":{"name":"Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129520893","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

Matching Images from Different Viewpoints with Deep Learning Based on LoFTR and MAGSAC++ 基于LoFTR和MAGSAC++的深度学习不同视点图像匹配

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582181

Liang Tian

{"title":"Matching Images from Different Viewpoints with Deep Learning Based on LoFTR and MAGSAC++","authors":"Liang Tian","doi":"10.1145/3582177.3582181","DOIUrl":"https://doi.org/10.1145/3582177.3582181","url":null,"abstract":"Matching 2D images from different viewpoints plays a crucial role in the fields of Structure-from-Motion and 3D reconstruction. However, image matching for assorted and unstructured images with a wide variety of viewpoints leads to difficulty for traditional matching methods. In this paper, we propose a Transformer-based feature matching approach to capture the same physical points of a scene from two images with different viewpoints. The local features of images are extracted by the LoFTR, which is a detector-free deep-learning matching model on the basis of Transformer. The subsequent matching process is realized by the MAGSAC++ estimator, where the matching results are summarized in the fundamental matrix as the model output. By removing image feature points with low confidence scores and applying the test time augmentation, our approach can reach a mean Average Accuracy 0.81340 in the Kaggle competition Image Matching Challenge 2022. This score ranks 45/642 in the competition leaderboard, and can get a silver medal in this competition. Our work could help accelerate the research of generalized methods for Structure-from-Motion and 3D reconstruction, and would potentially deepen the understanding of image feature matching and related fields.","PeriodicalId":170327,"journal":{"name":"Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128860032","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: 1

Penetration Point Detection for Autonomous Trench Excavation Based on Binocular Vision 基于双目视觉的自主挖沟穿透点检测

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582191

Jiangying Zhao, Y. Hu, Mingrui Tian, Xiaohua Xia, Peng Tan

引用次数: 0

Semi-supervised Defect Segmentation with Uncertainty-aware Pseudo-labels from Multi-branch Network 基于不确定性感知的多分支网络伪标签半监督缺陷分割

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582190

Dejene M. Sime, Guotai Wang, Zhi Zeng, Bei Peng

{"title":"Semi-supervised Defect Segmentation with Uncertainty-aware Pseudo-labels from Multi-branch Network","authors":"Dejene M. Sime, Guotai Wang, Zhi Zeng, Bei Peng","doi":"10.1145/3582177.3582190","DOIUrl":"https://doi.org/10.1145/3582177.3582190","url":null,"abstract":"Semi-supervised learning methods have recently gained considerable attention for training deep learning networks with limited labeled samples and additional large label-free samples. Consistency regularization and pseudo-labeling methods are among the most widely used semi-supervised learning methods. However, unreliable pseudo labels will largely limit the model’s performance when learning from unlabeled images. To alleviate this problem, we propose uncertainty-rectified pseudo labels generated from dynamically mixing predictions of multiple decoders with a shared encoder network for semi-supervised defect segmentation. We estimated the uncertainty as the prediction discrepancy between the average prediction and the output of each decoder head. The estimated uncertainty then guides the consistency training as well as the pseudo-label-based supervision. The proposed method achieved significant performance improvement over the fully supervised baseline and other state-of-the-art semi-supervised segmentation methods on similar labeled data proportions. We also performed an extensive ablation study to demonstrate that the proposed method performs well under various setups.","PeriodicalId":170327,"journal":{"name":"Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision","volume":"420 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794761","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

Case study of 3D Interactive Design of Nanjing Tongji Gate Wall 南京同济城门墙三维交互设计案例研究

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582189

Yang Cao, Yuan Gao, Ang Geng

引用次数: 0

Action Recognition with Non-Uniform Key Frame Selector 非统一关键帧选择器的动作识别

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI: 10.1145/3582177.3582182

Haohe Li, Chong Wang, Shenghao Yu, Chenchen Tao

引用次数: 0

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision 2023第五届图像处理与机器视觉国际会议论文集

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 1900-01-01 DOI: 10.1145/3582177

引用次数: 0