IG-YOLOv5-based underwater biological recognition and detection for marine protection

Abstract

Underwater biological detection is of great significance to marine protection. However, the traditional target detection techniques have some challenges, such as insufficient feature extraction for small targets and low feature utilization rate. To address these challenges, an underwater biological detection model IG-YOLOv5 based on the idea of feature reuse is proposed. An Improved-Ghost module with feature reuse is designed. The module adds batch normalization operations to the identity mapping branch using the Add operation with feature fusion and the Sigmoid Linear Unit activation function with smoother zeros. The proposed model uses the Improved-Ghost module to reconstruct the CSPDarknet structure of YOLOv5, so as to realize the lightweight and accuracy improvement of the model. In addition, in order to solve the problem of target size and shape change in underwater environment, the optimized loss function is Wise-IoU v3, which is used to evaluate the accuracy and robustness of detection results. The results show that the IG-YOLOv5 model performs well in the 2021URPPC data set, with 0.5 mAP reaching 74.2, 4.3% higher than that of YOLOv5 model, and 2.7 less floating-point operations. In a word, IG-YOLOv5 model has high accuracy and robustness in underwater target detection, and Wise-IoU index can evaluate the quality of target detection results more accurately, which is suitable for underwater robots, underwater monitoring, and other fields and has a practical application value.