Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection
Aiming at the problems of large image translation bias at the pixel-level adaptation, the risk of source-bias discrimination at the feature-level adaptation, and the inability of weakly supervised learning to balance detection accuracy and real- time performance, a diversified domain shifter and pse...
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Harbin University of Science and Technology Publications
2024-06-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2326 |
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| _version_ | 1849702325449392128 |
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| author | LI Chengyan ZHENG Qisen WANG Hao |
| author_facet | LI Chengyan ZHENG Qisen WANG Hao |
| author_sort | LI Chengyan |
| collection | DOAJ |
| description | Aiming at the problems of large image translation bias at the pixel-level adaptation, the risk of source-bias discrimination at the feature-level adaptation, and the inability of weakly supervised learning to balance detection accuracy and real- time performance, a diversified domain shifter and pseudo bounding box generator are proposed to gradually adjust the pre-training model. The adaptive cross-domain framework is gradually completed at pixel-level and feature-level. A diversified intermediate domain adjustment detection model is generated from the source domain by a domain shifter to bridge the domain gap and reduce the image translation bias. The intermediate domain is used as the supervised source domain, and the pseudo-labeled image adjustment detection model is generated by combining image-level annotations in the target domain to improve source-bias discrimination. A real-time object detector matching the cross-domain framework is constructed based on SSD algorithm to realize real-time object detection under weakly supervised conditions. The mAP on PASCAL VOC migrated to Clipart1k and other datasets is 0. 4% ~ 4. 7% better than the existing methods. The detection speed is 32 FPS ~47 FPS. This improves the accuracy and meets the requirements of real-time detection, and has better migration detection performance. |
| format | Article |
| id | doaj-art-9f75a6ddbe9c4a05a1b79d7fda460e01 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-9f75a6ddbe9c4a05a1b79d7fda460e012025-08-20T03:17:40ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832024-06-012903111910.15938/j.jhust.2024.03.002Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object DetectionLI Chengyan0ZHENG Qisen1WANG Hao2School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaAiming at the problems of large image translation bias at the pixel-level adaptation, the risk of source-bias discrimination at the feature-level adaptation, and the inability of weakly supervised learning to balance detection accuracy and real- time performance, a diversified domain shifter and pseudo bounding box generator are proposed to gradually adjust the pre-training model. The adaptive cross-domain framework is gradually completed at pixel-level and feature-level. A diversified intermediate domain adjustment detection model is generated from the source domain by a domain shifter to bridge the domain gap and reduce the image translation bias. The intermediate domain is used as the supervised source domain, and the pseudo-labeled image adjustment detection model is generated by combining image-level annotations in the target domain to improve source-bias discrimination. A real-time object detector matching the cross-domain framework is constructed based on SSD algorithm to realize real-time object detection under weakly supervised conditions. The mAP on PASCAL VOC migrated to Clipart1k and other datasets is 0. 4% ~ 4. 7% better than the existing methods. The detection speed is 32 FPS ~47 FPS. This improves the accuracy and meets the requirements of real-time detection, and has better migration detection performance.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2326real-time object detectionweakly supervised learningdomain adaptationimage translation networkssd algorithm |
| spellingShingle | LI Chengyan ZHENG Qisen WANG Hao Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection Journal of Harbin University of Science and Technology real-time object detection weakly supervised learning domain adaptation image translation network ssd algorithm |
| title | Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection |
| title_full | Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection |
| title_fullStr | Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection |
| title_full_unstemmed | Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection |
| title_short | Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection |
| title_sort | domain diversification progressive cross domain weakly supervised real time object detection |
| topic | real-time object detection weakly supervised learning domain adaptation image translation network ssd algorithm |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2326 |
| work_keys_str_mv | AT lichengyan domaindiversificationprogressivecrossdomainweaklysupervisedrealtimeobjectdetection AT zhengqisen domaindiversificationprogressivecrossdomainweaklysupervisedrealtimeobjectdetection AT wanghao domaindiversificationprogressivecrossdomainweaklysupervisedrealtimeobjectdetection |