基于改进的YOLOv5的航拍图像中小目标检测算法

A small target detection algorithm based on improved YOLOv5 in aerial image

目前基于无人机航拍的目标检测技术广泛应用于军事和民用领域,但因其存在成像距离远、高空拍摄图像模糊和目标信息占比小等问题,目标检测准确率不高。针对这一问题,提出一种基于YOLOv5的改进算法。该算法首先在数据增强方面对原始图像进行加雾处理,提高其在雾天的鲁棒性;其次通过融合CBAM模块,来增强不同通道和空间的重要性;再者将原算法中的SPP更换为ASPP,以减小池化操作对特征信息的影响;最后在FPN结构中增加一层检测头,用于更细粒度的检测目标。以YOLOv5s为Baseline,实验表明,改进后的算法比原算法的mAP_0.5提高了6.9%,可以有效应用于航拍小目标的检测。

At present,the target detection technology based on UAV aerial photography is widely used in military and civil fields,but the accuracy of target detection is not high because of the long imag-ing distance,blurred images taken at high altitudes,and small proportion of target information.To solve this problem,an improved algorithm based on YOLOv5 is proposed.Firstly,the original image is fogged to improve its robustness on foggy days.Secondly,the importance of different channels and spaces is enhanced through the integration of CBAM modules.Furthermore,the SPP in the original algorithm is replaced by the ASPP to reduce the influence of pooling operation on feature information.Finally,a detection head is added to the FPN structure to detect targets with finer granularity.Taking YOLOv5s as baseline,the experiment proves that the improved algorithm increases mAP_0.5 by 6.9%in comparison to the original algorithm,and can be effectively applied to the detection of small targets in aerial photography.