改进YOLOv5的无人机影像车辆检测方法

Vehicle Detection Method of UAV Imagery Based on Improved YOLOv5

针对传统遥感影像车辆目标检测算法易受干扰、鲁棒性较差且在实际应用当中会产生一定的漏检与误检现象等问题,提出了一种基于改进YOLOv5s的轻量级无人机遥感影像车辆目标检测算法。以YOLOv5s为基线模型,根据车辆目标长宽比相对固定的特点,对锚框尺寸进行修正,提高了锚框与车辆目标的契合度;针对无人机影像中车辆密集情况,进行了加权框融合改进,对检测框合并,解决了预测框计数不准确的问题;由于车辆目标具有多变性,通过增加注意力机制网络,提升了模型识别车辆的速度和准确性。研究表明,改进的YOLOv5s模型可以实现实时准确的无人机影像车辆检测。

Aiming at the problems that the traditional remote sensing image vehicle target detection algorithm is easy to be interfered,has poor robustness,and will produce certain missing and false detection phenomena in practical applications,a lightweight Unmanned Aerial Vehicle(UAV)remote sensing image vehicle target detection algorithm based on improved YOLOv5s is proposed.Taking YOLOv5s as the baseline model,according to the relatively fixed aspect ratio of the vehicle target,the size of the anchor frame is modified to improve the fit between the anchor frame and the vehicle target.In view of the dense vehicles in the UAV image,the weighted frame fusion is improved,and the detection frame is merged to solve the problem of inaccurate prediction frame count.Due to the variability of vehicle targets,the speed and accuracy of vehicle recognition are improved by adding attention mechanism network.The research results show that the improved YOLOv5s model can achieve real-time and accurate vehicle detection of UAV images.