Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion
Hard examples for visual perception can improve the performance of object detection effectively in autonomous driving scenes. However, they are dif ficult and hard to obtain in the real world. A hard example mining method for visual perception based on multisensory fusion was presented. The obstacle...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Drive for Locomotives
2021-11-01
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| Series: | 机车电传动 |
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| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.06.013 |
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| _version_ | 1849729308417851392 |
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| author | Jun LIN Wenbo PAN Jun YOU Yanghan XU |
| author_facet | Jun LIN Wenbo PAN Jun YOU Yanghan XU |
| author_sort | Jun LIN |
| collection | DOAJ |
| description | Hard examples for visual perception can improve the performance of object detection effectively in autonomous driving scenes. However, they are dif ficult and hard to obtain in the real world. A hard example mining method for visual perception based on multisensory fusion was presented. The obstacles target segmented from radar point clouds was used to verify the image detection targets.Hard samples of image object detection and unlabeled samples in the real open world can be identi fied by the mapping relationship of multi-sensors based on actual obstacles. These hard samples were for training a new object detection model and remote deployment through the cloud-side collaboration mechanism to realize the optimization and iterative update of the model. Experiments show that hard samples in the mining autopilot scenes can be effectively collected by this method, and be used to improve the performance of object detection through incremental transfer learning signi ficantly. In addition, the algorithm also has important guiding signi ficance for autonomous driving scenarios in rail transit and other fields. |
| format | Article |
| id | doaj-art-79f6a471d84d47898a0286fad119c2c6 |
| institution | DOAJ |
| issn | 1000-128X |
| language | zho |
| publishDate | 2021-11-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-79f6a471d84d47898a0286fad119c2c62025-08-20T03:09:15ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2021-11-010939922773180Hard Example Mining Method for Visual Perception Based on Multi-sensor FusionJun LINWenbo PANJun YOUYanghan XUHard examples for visual perception can improve the performance of object detection effectively in autonomous driving scenes. However, they are dif ficult and hard to obtain in the real world. A hard example mining method for visual perception based on multisensory fusion was presented. The obstacles target segmented from radar point clouds was used to verify the image detection targets.Hard samples of image object detection and unlabeled samples in the real open world can be identi fied by the mapping relationship of multi-sensors based on actual obstacles. These hard samples were for training a new object detection model and remote deployment through the cloud-side collaboration mechanism to realize the optimization and iterative update of the model. Experiments show that hard samples in the mining autopilot scenes can be effectively collected by this method, and be used to improve the performance of object detection through incremental transfer learning signi ficantly. In addition, the algorithm also has important guiding signi ficance for autonomous driving scenarios in rail transit and other fields.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.06.013hard example miningmulti-sensor fusiondeep learningvisual perceptionautonomous driving |
| spellingShingle | Jun LIN Wenbo PAN Jun YOU Yanghan XU Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion 机车电传动 hard example mining multi-sensor fusion deep learning visual perception autonomous driving |
| title | Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion |
| title_full | Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion |
| title_fullStr | Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion |
| title_full_unstemmed | Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion |
| title_short | Hard Example Mining Method for Visual Perception Based on Multi-sensor Fusion |
| title_sort | hard example mining method for visual perception based on multi sensor fusion |
| topic | hard example mining multi-sensor fusion deep learning visual perception autonomous driving |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.06.013 |
| work_keys_str_mv | AT junlin hardexampleminingmethodforvisualperceptionbasedonmultisensorfusion AT wenbopan hardexampleminingmethodforvisualperceptionbasedonmultisensorfusion AT junyou hardexampleminingmethodforvisualperceptionbasedonmultisensorfusion AT yanghanxu hardexampleminingmethodforvisualperceptionbasedonmultisensorfusion |