Affiliation:
1. College of Information Science, Shanghai Ocean University, Shanghai 201306, China
2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
3. South China Sea Institute of Planning and Environmental Research, State Oceanic Administration, Guangzhou 510310, China
4. Technology Innovation Center for South China Sea Remote Sensing, Surveying and Mapping Collaborative Application, Ministry of Natural Resources, Guangzhou 510310, China
Abstract
Underwater object detection, as the principal means of underwater environmental sensing, plays a significant part in the marine economic, military, and ecological fields. Due to the degradation problems of underwater images caused by color cast, blurring, and low contrast, we proposed a model for underwater object detection based on YOLO v7. In the presented detection model, an enhanced image branch was constructed to expand the feature extraction branch of YOLOv7, which could mitigate the feature degradation issues existing in the original underwater images. The contextual transfer block was introduced to the enhanced image branch, following the underwater image enhancement module, which could extract the domain features of the enhanced image, and the features of the original images and the enhanced images were fused before being fed into the detector. Focal EIOU was adopted as a new model bounding box regression loss, aiming to alleviate the performance degradation caused by mutual occlusion and overlapping of underwater objects. Taking URPC2020 and UTDAC2020 (Underwater Target Detection Algorithm Competition 2020) datasets as experimental datasets, the performance of our proposed model was compared against with other models, including YOLOF, YOLOv6 v3.0, DETR, Swin Transformer, and InternImage. The results show that our proposed model presents a competitive performance, achieving 80.71% and 86.32% in mAP@0.5 on URPC2020 and UTDAC2020, respectively. Comprehensively, the proposed model is capable of effectively mitigating the problems encountered in the task of object detection in underwater images with degraded features and exhibits great advancement.
Funder
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources
Capacity Development for Local College Project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
1 articles.
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