A detection-regression based framework for fish keypoints detection

Abstract

Applying computer vision technology in aquaculture can improve the efficiency of fish detection and health monitoring as well as optimize aquaculture management and profit. Keypoints on fish bodies are important biological indicators that can be used to calculate the individual size, mass, and behavior. However, only a few relevant studies have been conducted in this regard, and they mainly focus on detecting keypoints for stereo matching. Traditional keypoint detection methods exhibit low efficiency, poor accuracy, and weak robustness in underwater environments. Accordingly, this study proposes a new method based on object detection and point regression models to locate fish keypoints. First, individual fish are detected by employing a commonly used object detection model, YOLOv5. The detection accuracy is further improved by enhancing the network neck. In the second stage, a deep learning model for locating fish keypoints is constructed by implementing weight allocation and distribution-aware strategy in the matched left and right bounding boxes to improve on the previous work of Lite-HRNet, which was originally designed for capturing human body keypoints. The experimental results show that the proposed method can effectively detect individual underwater fish and accurately estimate the keypoints. The source code and the labeled datasets for fish detection and keypoint location are provided. The code is available at https://github.com/oucvisionlabsanya/fish_keypoint_detection.git.