Underwater target detection algorithm based on feature enhancement and feature fusion

Abstract

Abstract In the field of target detection, identifying underwater targets remains a formidable challenge due to the complexity of the underwater environment and the substantial variations in target scale. To tackle the difficulties of extracting complex features in such conditions, this study introduces a novel feature extraction module built upon the YOLOv7 model. The module incorporates a custom-designed information attention mechanism and deformable convolution, enhancing the network’s ability to capture and process intricate underwater features. Additionally, a feature enhancement and interaction module was developed further to strengthen the extraction and utilization of key features. An adaptive feature fusion module was also introduced, allowing for dynamic adjustment of fusion weights based on the characteristics of the input features, thereby enabling more efficient and accurate multi-scale feature fusion. Experimental results reveal that, compared to the original YOLOv7 model on the URPC2020 dataset, the proposed method achieves improvements of 3.3% and 2.7% in mAP50 and mAP50:95 metrics, respectively. These significant performance gains highlight the effectiveness of the proposed approach in underwater target detection and provide valuable new directions for research and applications in this domain.