YoloXT: A object detection algorithm for marine benthos

Abstract

In recent years, the marine economy has developed rapidly, and human demand for marine resources has increased greatly. At present, target detection technology has a wide range of applications and prospects in seabed observation and ocean engineering. However, the accuracy and robustness of existing target detection methods are low due to the complex underwater environment, poor lighting, and poor quality of undersea images and videos. To solve these problems, this paper proposes YoloXT, a new quantitative identification method for marine benthos. YoloXT introduces the DECA (Deformable Coordinate Attention) module, which expands the spatial awareness in feature extraction and can learn image features more effectively. Meanwhile FPST-PAN (Feature Pyramid S2win Transformer, Improved Path Aggregation Network) was proposed to deal with the problem of marine benthic target diversity. It further integrates deep and shallow features through multi-scale skip-connection and Transformer and improves the model's ability to deal with complex and changeable marine environments. Finally, the positive and negative sample assignment strategy OAA (Optimal Anchor Assignment) applied to the detection head is proposed. It effectively avoids the problem of unbalanced distribution of positive and negative samples caused by traditional sample assignment methods and marine benthos image noise. Experiments on the IOC-URPC dataset show that the mAP of YoloXT is 3.9% higher than that of YoloX, reaching 70.9%. YoloXT has demonstrated excellent performance in quantitative identification task of marine organisms, which can effectively contribute to the exploitation and conservation of marine resources. The source code is publicly available at https://github.com/F1veZhang/YOLOXT.