Underwater moving target detection and tracking based on enhanced you only look once and deep simple online and realtime tracking strategy

Addressing the challenges posed by factors like light attenuation, turbid water quality, large target scale changes, and dense distribution in underwater moving target images, we propose an enhanced strategy combining YOLOv5 (You Only Look Once version 5) and Deep SORT (Simple Online and Realtime Tracking) for target detection and tracking. Initially, various image enhancement techniques are employed to improve detection performance, while a Deep Convolutional Generative Adversarial Networks (DCGAN) approach augments the small underwater image dataset. Subsequently, we enhance the YOLOv5 method by integrating convolutional attention module into the detection network to enhance target salience in large-scale scenes. Additionally, a tiny target detection head is introduced to enhance the detector's capability in adapting to scale changes, particularly for small objects. Finally, the Deep SORT algorithm is integrated for tracking detected targets. Experimental comparisons with state-of-the-art methods validate the efficacy of the proposed approach. Our method achieves a detection accuracy of 94.3% on a self-made underwater fish target dataset, with a detection speed of 71FPS and robust tracking performance. Moreover, its applicability extends to various underwater targets, demonstrating superior performance.