An Improved YOLOV5 Based on Triplet Attention and Prediction Head Optimization for Marine Organism Detection on Underwater Mobile Platforms

Abstract

Machine vision-based automatic detection of marine organisms is a fundamental task for the effective analysis of production and habitat changes in marine ranches. However, challenges of underwater imaging, such as blurring, image degradation, scale variation of marine organisms, and background complexity, have limited the performance of image recognition. To overcome these issues, underwater object detection is implemented by an improved YOLOV5 with an attention mechanism and multiple-scale detection strategies for detecting four types of common marine organisms in the natural scene. An image enhancement module is employed to improve the image quality and extend the observation range. Subsequently, a triplet attention mechanism is introduced to the YOLOV5 model to improve the feature extraction ability. Moreover, the structure of the prediction head of YOLOV5 is optimized to capture small-sized objects. Ablation studies are conducted to analyze and validate the effective performance of each module. Moreover, performance evaluation results demonstrate that our proposed marine organism detection model is superior to the state-of-the-art models in both accuracy and speed. Furthermore, the proposed model is deployed on an embedded device and its processing time is less than 1 s. These results show that the proposed model has the potential for real-time observation by mobile platforms or undersea equipment.