Underwater trash detection algorithm based on image enhancement and improved RT-DETR

doi:10.13474/j.cnki.11-2246.2025.0821

Abstract

Abstract: With the increasingly prominent problem of marine environmental pollution,rapid detection and cleaning of underwater garbage are particularly urgent.A new underwater garbage detection algorithm based on improved RT-DETR is proposed to address the issues of poor image quality,severe exposure to light,overlapping and varying shapes that lead to poor detection performance.Aiming at the problems of color cast and low contrast in images,an enhancement algorithm combining contrast enhancement and adaptive color compensation is designed for image preprocessing.In response to the demand for lightweight mobile device models,the FasterNet Block module is introduced to improve the backbone network and reduce the number of model parameters.To address the issue of weak lighting in underwater environments,the HS-FPN advanced filtering feature fusion pyramid fusion strategy is adopted to solve the problems of severe feature loss and low discrimination.For small targets in images,a GELAN generalized efficient layer aggregation network is adopted to improve the representation ability of the model.To address the issue of large differences in garbage size caused by spatial location,an Inner-ShapeIoU loss function combining Inner-IoU and ShapeIoU is introduced to improve the robustness of object detection.The experimental results show that the proposed method effectively solves the problems of image color cast and low contrast.Compared with the original model,the detection accuracy has been improved by 3.9 percent,and the number of parameters has been reduced by 26.3 percent.The underwater garbage detection performance is superior.

Key words: underwater trash detection, deep learning, image enhancement, RT-DETR

CLC Number:

P237

LI Chao, LIU Qingyi, ZHANG Jiawei, SHI Yong, YANG Min. Underwater trash detection algorithm based on image enhancement and improved RT-DETR[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 128-136.

References

[1] 李陵云,王词稼,褚晓婷,等.我国海洋塑料垃圾治理体系与成效[J].海洋环境科学,2024,43(2):161-166.
[2] HIPOLITO J C,SARRAGA ALON A,AMORADO R V,et al.Detection of underwater marine plastic debris using an augmented low sample size dataset for machine vision system: a deep transfer learning approach[C]//Proceedings of the 19th IEEE Student Conference on Research and Development (SCOReD).Kota Kinabalu: IEEE,2021: 82-86.
[3] FULTON M,HONG J,ISLAM M J,et al.Robotic detection of marine litter using deep visual detection models[C]//Proceedings of 2019 International Conference on Robotics and Automation (ICRA).Montreal:IEEE,2019:5752-5758.
[4] TIAN Manjun,LI Xiali,KONG Shihan,et al.A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot[J].Frontiers of Information Technology & Electronic Engineering,2022,23(8):1217-1229.
[5] 袁红春,臧天祺.基于注意力机制及Ghost-YOLOv5的水下垃圾目标检测[J].环境工程,2023,41(7):214-221.
[6] ZHAO Yian,Lü Wenyu,XU Shangliang,et al.DETRs beat YOLOs on real-time object detection[EB/OL].[2024-12-23].https://arxiv.org/abs/2304.08069v3.
[7] PENG Y T,COSMAN P C.Underwater image restoration based on image blurriness and light absorption[J].IEEE Transactions on Image Processing,2017,26(4):1579-1594.
[8] WANG C Y,MARK LIAO H Y,WU Y H,et al.CSPNet:a new backbone that can enhance learning capability of CNN[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).Seattle:IEEE,2020:1571-1580.
[9] WANG C Y,LIAO H M,YEH I H.Designing network design strategies through gradient path analysis[EB/OL].[2024-12-23].https://arxiv.org/abs/2211.04800v1.
[10] ZHANG H,ZHANG S.Shape-IoU:more accurate metric considering bounding box shape and scale[EB/OL].[2024-12-15].https://doi.org/10.48550/arXiv.2312.17663.
[11] HONG J,FULTON M,SATTAR J.TrashCan:a semantically-segmented dataset towards visual detection of marine debris[EB/OB].[2024-12-18].https://doi.org/10.48550/arXiv.2007.08097.
[12] PANETTA K,GAO Chen,AGAIAN S.Human-visual-system-inspired underwater image quality measures[J].IEEE Journal of Oceanic Engineering,2016,41(3):541-551.
[13] YANG Miao,SOWMYA A.An underwater color image quality evaluation metric[J].IEEE Transactions on Image Processing,2015,24(12):6062-6071.
[14] WANG Shiqi,MA Kede,YEGANEH H,et al.A patch-structure representation method for quality assessment of contrast changed images[J].IEEE Signal Processing Letters,2015,22(12):2387-2390.
[15] DREWS P,DO NASCIMENTO E,MORAES F,et al.Transmission estimation in underwater single images[C]//Proceedings of 2013 IEEE International Conference on Computer Vision Workshops.Sydney:IEEE,2013:825-830.
[16] KANG Yaozu,JIANG Qiuping,LI Chongyi,et al.A perception-aware decomposition and fusion framework for underwater image enhancement[J].IEEE Transactions on Circuits and Systems for Video Technology,2023,33(3):988-1002.
[17] ZHUANG Peixian,WU Jiamin,PORIKLI F,et al.Underwater image enhancement with hyper-Laplacian reflectance priors[J].IEEE Transactions on Image Processing,2022,31:5442-5455.
[18] WANG Huibai,WANG Yuxuan.Improved glove defect detection algorithm based on YOLOv5 framework[C]//Proceedings of the 6th IEEE Advanced Information Technology,Electronic and Automation Control Conference (IAEAC).Beijing:IEEE,2022:1192-1197.
[19] WANG C Y,BOCHKOVSKIY A,LIAO H M.YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Vancouver:IEEE,2023:7464-7475.
[20] WANG C Y,YEH I H,LIAO H M.YOLOv9:learning what you want to learn using programmable gradient information[EB/OL].[2024-12-26].https://arxiv.org/abs/2402.13616v2.