U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866918176573358080 |
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| author | Liu, Zhuoyan Wang, Bo Wang, Bing Li, Ye |
| author_facet | Liu, Zhuoyan Wang, Bo Wang, Bing Li, Ye |
| contents | Underwater object detection has higher requirements of running speed and deployment efficiency for the detector due to its specific environmental challenges. NMS of two- or one-stage object detectors and transformer architecture of query-based end-to-end object detectors are not conducive to deployment on underwater embedded devices with limited processing power. As for the detrimental effect of underwater color cast noise, recent underwater object detectors make network architecture or training complex, which also hinders their application and deployment on unmanned underwater vehicles. In this paper, we propose the Underwater DECO with improved deNoising training (U-DECN), the query-based end-to-end object detector (with ConvNet encoder-decoder architecture) for underwater color cast noise that addresses the above problems. We integrate advanced technologies from DETR variants into DECO and design optimization methods specifically for the ConvNet architecture, including Deformable Convolution in SIM and Separate Contrastive DeNoising Forward methods. To address the underwater color cast noise issue, we propose an Underwater Color DeNoising Query method to improve the generalization of the model for the biased object feature information by different color cast noise. Our U-DECN, with ResNet-50 backbone, achieves the best 64.0 AP on DUO and the best 58.1 AP on RUOD, and 21 FPS (5 times faster than Deformable DETR and DINO 4 FPS) on NVIDIA AGX Orin by TensorRT FP16, outperforming the other state-of-the-art query-based end-to-end object detectors. The code is available at https://github.com/LEFTeyex/U-DECN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05780 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training Liu, Zhuoyan Wang, Bo Wang, Bing Li, Ye Computer Vision and Pattern Recognition I.4 Underwater object detection has higher requirements of running speed and deployment efficiency for the detector due to its specific environmental challenges. NMS of two- or one-stage object detectors and transformer architecture of query-based end-to-end object detectors are not conducive to deployment on underwater embedded devices with limited processing power. As for the detrimental effect of underwater color cast noise, recent underwater object detectors make network architecture or training complex, which also hinders their application and deployment on unmanned underwater vehicles. In this paper, we propose the Underwater DECO with improved deNoising training (U-DECN), the query-based end-to-end object detector (with ConvNet encoder-decoder architecture) for underwater color cast noise that addresses the above problems. We integrate advanced technologies from DETR variants into DECO and design optimization methods specifically for the ConvNet architecture, including Deformable Convolution in SIM and Separate Contrastive DeNoising Forward methods. To address the underwater color cast noise issue, we propose an Underwater Color DeNoising Query method to improve the generalization of the model for the biased object feature information by different color cast noise. Our U-DECN, with ResNet-50 backbone, achieves the best 64.0 AP on DUO and the best 58.1 AP on RUOD, and 21 FPS (5 times faster than Deformable DETR and DINO 4 FPS) on NVIDIA AGX Orin by TensorRT FP16, outperforming the other state-of-the-art query-based end-to-end object detectors. The code is available at https://github.com/LEFTeyex/U-DECN. |
| title | U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training |
| topic | Computer Vision and Pattern Recognition I.4 |
| url | https://arxiv.org/abs/2408.05780 |