重庆理工大学学报2024，Vol.38Issue(5)：212-219,8.DOI:10.3969/j.issn.1674-8425(z).2024.03.023
雾环境下的船舶目标检测研究
Research on ship object detection in foggy environments
摘要
Abstract
To effectively avoid ship collisions and overcome difficulty in ship identification, and low detection accuracy in foggy environments, this paper first builds a dataset for ship detection in foggy environments. Then, improvements are made on the YOLOv5 model. Specifically, the GSConv module is employed to replace the CBS module in the Head section to make the depth separable convolution closer to the separable convolution, improving model accuracy. The Slim-Neck paradigm is introduced to further boost the model's average accuracy and reduce computational complexity. Additionally, the binary cross-entropy loss function is replaced with a polynomial loss function to enhance the model's accuracy. The SIoU Loss is introduced to address the deficiency in direction between the real box and predicted box, thereby improving training speed and inference accuracy. Our experimental results show the model reaches 95. 7％ in mAP0. 5 score, 0. 9％ higher than that of the baseline YOLOv5 model while the FLOPs is down by 2. 1G. Our study demonstrates the fog-based ship detection model achievesa better accuracy and has a lighter model structure and thus it has great potentials for application in improving the accuracy and efficiency of ship detection in foggy environments.关键词
深度学习/目标检测/智能船舶/雾环境Key words
deep learning/object detection/smart ships/foggy environments分类
信息技术与安全科学引用本文复制引用
肖晶晶,樊博彦,杨雨婷..雾环境下的船舶目标检测研究[J].重庆理工大学学报,2024,38(5):212-219,8.基金项目
厦门市重大项目-船舶智能运维系统集成与安全感知研发与产业化示范(21CZB014HJ08) （21CZB014HJ08）
