关键词: 关键词: 复杂天气环境, YOLOv8, 深度学习, 船舶检测, 目标识别

Abstract: Abstract: To address the problems of missed detection and false detection in maritime ship detection tasks under complex weather environments such as rain, fog, and low light, a ship detection method based on an enhanced YOLOv8 algorithm for maritime complex weather environments was proposed. First, a Cross-Granularity Local Global Attention Fusion Block (CGLGAFB) was proposed. A refined local and global feature fusion mechanism was constructed and multi-path feature fusion strategies were combined to integrate multi-source feature information from different levels. The model's feature fusion capability was enhanced while noise interference and information redundancy were suppressed. Then, the original C2f (Faster Implementation of CSP Bottleneck with 2 convolutions) module was improved to an adaptive mixed C2f module (C2f-Adaptive Mixer Block, C2f-AMB). Through depth convolution branches with adaptive receptive field adjustment capabilities, target features of different scales and complex spatial structures can be captured more flexibly and efficiently, thereby enhancing feature extraction capabilities. Finally, a Multi-scale Spatial Perception Pyramid (MSPP) module was proposed to replace SPPF. Dilated convolutions with different dilation rates were utilized to construct multi-scale receptive fields, obtain comprehensive contextual information, and reduce the omission of key information. Experiments on the enhanced dataset SeaShips_aug show that the improved detection algorithm achieves mAP@50 and recall rates of 84.7% and 79.3% respectively, which are 2.6 and 3.9 percentage points higher than the baseline YOLOv8 model. The results prove that the enhanced model is more suitable for maritime ship detection tasks in complex weather environments.

Key words: Keywords: complex weather conditions, YOLOv8, deep learning, ship detection, target recognition