An Improved YOLOv5-Based Lightweight Submarine Target Detection Algorithm

Abstract

Submarine recognition plays a critical role in maritime security and military defense. However, traditional submarine recognition algorithms face limitations in feature representation capability and robustness. Additionally, deploying deep learning methods on embedded and mobile platforms presents a bottleneck. To address these challenges, we propose an innovative and practical approach—an improved YOLOv5-based lightweight submarine automatic recognition detection algorithm. Our method leverages the Feature Pyramid based on MobileNetV3 and the C3_DS module to reduce computation and parameter complexity while ensuring high precision in submarine recognition. The integration of the adaptive neck from the SA-net strategy further mitigates missed detections, significantly enhancing the accuracy of submarine target detection and recognition. We evaluated our improved model on a submarine dataset, and the results demonstrate remarkable advancements in Precision, Recall, and mAP0.5, with respective increases of 8.54%, 6.02%, and 3.36%. Moreover, we achieved a notable reduction of 34.1% in parameter quantity and 67.9% in computational complexity, showcasing its lightweight effects. Overall, our proposed method introduces novel improvements to submarine recognition, addressing existing limitations and offering practical benefits for real-world deployment on embedded and mobile platforms. The enhanced performance in precision and recall metrics, coupled with reduced computational requirements, emphasizes the significance of our approach in enhancing maritime security and military applications.