DY-YOLOv5:基于多重注意力机制的航拍图像目标检测

DY-YOLOv5:Target Detection for Aerial Image Based on Multiple Attention

针对无人机航拍图像中目标小、尺度不一和背景复杂等导致检测精度低的问题,提出一种基于改进YOLOv5的无人机航拍图像目标检测算法DY-YOLOv5。该算法在检测头部分利用具有多重注意力机制的目标检测头方法Dynamic Head,提升检测头在复杂背景下的检测表现。在原模型neck部分增加一次上采样和Concat操作,并执行一个包含极小、小、中目标的多尺度特征检测,提升模型对中、小目标的特征提取能力。引入密集卷积网络DenseNet,将其与YOLOv5s主干网络的C3模块进行融合,提出C3_DenseNet模块,以加强特征传递并预防模型过拟合。在VisDrone2019数据集上应用DY-YOLOv5算法,平均精度均值(mAP)达到了43.9%,较原YOLOv5算法提升了11.4个百分点。召回率(Recall)为41.7%,较原算法提升了9.0个百分点。实验结果证明,改进算法显著提高了无人机航拍图像目标检测的精度。

Aiming at the problem of low detection accuracy caused by small targets,different scales and complex backgrounds in UAV aerial images,a target detection algorithm for UAV aerial images based on improved YOLOv5 is proposed.The algorithm introduces a target detection head method Dynamic Head with multiple attention mechanisms to replace the original detection head and improves the detection performance of the detection head in complex backgrounds.An upsampling and Concat operation is added to the neck part of the original model,and a multi-scale feature detection including minimal,small and medium targets is performed to improve the feature extraction ability of the model for medium and small targets.DenseNet is introduced and integrated with the C3 module of YOLOv5s backbone network to propose the C3_DenseNet module to enhance feature transfer and prevent model overfitting.The DY-YOLOv5 algorithm is applied to the VisDrone 2019 dataset,and the mean average precision(mAP) reaches 43.9%,which is 11.4 percentage points higher than the original algorithm.The recall rate(Recall) is 41.7%,which is 9.0 percentage points higher than the original algorithm.Experimental results show that the improved algorithm significantly improves the accuracy of target detection in UAV aerial images.