用于船桥碰撞预警的复杂场景下视觉船舶检测

Vision-based vessel detection for vessel-bridge collision warnings under complex scenes

为准确识别航道船舶,实现桥梁防撞预警,结合实拍图像和数据增强建立了针对桥梁航道船舶的图像数据集,包括远距离、多目标以及可视度低等复杂场景.然后,以YOLOv5模型作为基本检测器并对其网络结构进行改进,主要改进包括将主干网络中的C3模块替换为C2f模块,在特征融合网络中嵌入SE注意力机制,采用K-means++聚类算法对数据集的先验框进行优化.最后,以PyTorch为深度学习框架对改进YOLOv5模型进行训练和验证.结果表明,改进YOLOv5模型对重点检测船舶的平均精度达到99.4%,较原始YOLOv5模型提高了11.1%,检测速度达到102帧/s,可为复杂通航场景下船桥碰撞预警提供可靠、高效的支撑.

To enable accurate vessel recognition for bridge collision avoidance and early warning,an image dataset for vessels in bridge channels is established using cameras and data augmentation.This dataset includes complex scenarios such as long distances,multiple targets,and low visibility.Subsequently,the you-only-look-once version 5(YOLOv5)model is employed as the basic detector,and several modifications are applied to its network structure.Key enhancements involve replacing C3 modules in the backbone network with C2f modules,integrating the squeeze-excitation attention mechanism into the feature fusion network,and optimizing the prior anchors of the dataset using the K-means++clustering algorithm.Finally,the modified model undergoes training and validation using PyTorch as the deep learning framework.Results demonstrate that the mean average precision for crucial vessels in the modified YOLOv5 model reaches 99.4%,representing an 11.1%improvement compared to the original YOLOv5 model.Additionally,the inference speed is measured at 102 frame/s.The established YOLOv5 model is a reliable and efficient cornerstone for warning against vessel-bridge collisions in complex navigable scenes.