Weighted multi-error information entropy based you only look once network for underwater object detection

Abstract

Underwater object detection is considered as one of the most challenging issues in computer vision. In this paper, a weighted multi-error information entropy based YOLO (You Only Look Once) network is proposed to address underwater illumination noise affecting the detection accuracy. First, underwater illumination is essentially structural and non-uniform, and it is modeled as an independent and piecewise identical distribution, which is a generic noise model to describe the complex underwater illuminating environment and accommodates the traditional Gaussian distribution as a special case. Second, assisted by the proposed illumination noise model, a minimum weighted error entropy criterion, which is an information-theoretic learning method, is introduced into the loss function of YOLO network, and then the network parameters are trained and optimized to improve the detection performance. Furthermore, a multi-error processing strategy is simultaneously used to handle vector errors during information back-propagation in order to accelerate convergence. Experiments on underwater object detection datasets including URPC2018, URPC2019 and Enhanced dataset, show the proposed weighted multi-error information entropy based YOLOv8 network gets mean average precision (MAP) of 88.7%, 91.8% and 96.7% respectively, and average frames per second (FPS) of 116.6. These two evaluation metrics are better than the baseline YOLOv8 and the existing advanced non-YOLO approaches by at least 5.2% and 5.3% respectively. The results verify the effectiveness and superiority of the proposed network for underwater object detection in complex underwater environment.