基于改进YOLOv3的遥感图像小目标检测

Remote sensing image small target detection based on improved YOLOv3

YOLOv3算法被广泛地应用于目标检测任务。虽然在YOLOv3基础上改进的一些算法取得了一定的成果,但是仍存在表征能力不足且检测精度不高的问题,尤其对小目标的检测还不能满足需求。针对上述问题,提出了一种改进YOLOv3的遥感图像小目标检测算法。首先,使用K均值聚类变换(K-means-T)算法优化锚框的大小,从而提升先验框和真实框之间的匹配度;其次,优化置信度损失函数,以解决难易样本分布不均衡的问题;最后,引入注意力机制来提高算法对细节信息的感知能力。在RSOD数据集上进行实验的结果显示,与原始的YOLOv3算法、YOLOv4算法相比,所提算法在小目标“飞机(aircraft)”类上的平均精确率(AP)分别提高了7.3个百分点和5.9个百分点。这表明所提算法能够有效检测遥感图像小目标,具有更高的准确率。

YOLOv3(You Only Look Once version 3)algorithm is widely used in target detection tasks.Although some improved algorithms based on YOLOv3 have achieved some results,there are still problems of insufficient representation ability and low detection accuracy,especially for the detection of small targets.In order to solve the above problems,a small target detection algorithm for remote sensing images based on YOLOv3 was proposed.Firstly,K-means Transformation(Kmeans-T)algorithm was used to optimize the size of anchor box,so that the matching degree between the priori box and ground truth box was improved.Secondly,the confidence loss function was optimized to solve the problem of uneven distribution of hard and easy samples.Finally,attention mechanism was introduced to improve the algorithm’s ability to perceive the detailed information.Results of the experiments carried out on RSOD dataset show that compared with the original YOLOv3 algorithm and YOLOv4 algorithm,the proposed algorithm has the detection Average Precision(AP)on the small target class“aircraft”increased by 7.3 percentage points and 5.9 percentage points respectively,illustrating that the proposed improved algorithm can detect small targets in remote sensing images effectively,with higher accuracy.