Study on optimization model of lightweight ship target detection algorithm based on improved YOLOv5

As the demand for enhanced maritime safety continues to grow, ship target detection technology has become increasingly crucial in the domain of ocean surveillance. Conventional target detection methods face challenges such as high computational demands and reduced detection efficiency in ship identification scenarios. This study introduces an optimized lightweight ship detection algorithm, building upon the YOLOv5 framework. By incorporating streamlined model architecture and parameter tuning, the model retains YOLOv5’s high accuracy while minimizing computational overhead and boosting detection speed. The proposed model integrates deep separable convolution and pruning techniques, effectively shrinking the network size without compromising detection precision. For system validation, a publicly available dataset featuring a variety of ships is employed in the experimental phase. The simulation outcomes indicate that the refined model sustains high detection accuracy while substantially decreasing computational load. Experimental findings reveal that the model achieves a mean Average Precision (mAP) of 92.3% and accelerates detection to 45 frames per second (FPS), marking a 15% improvement over the original YOLOv5 algorithm. This research presents a novel approach for ship detection in maritime monitoring, offering significant practical application potential.