基于改进YOLOv5的无人机检测方法

Drone Detection Method Based on Improved YOLOv5

针对小尺寸无人机目标检测精度低,且深层网络的参数量大、内存占用高等问题,提出一种基于改进YOLOv5的无人机检测方法。首先,调整了YOLOv5多尺度预测层的个数,裁剪掉冗余网络层,有效减少网络参数量,提高无人机检测速度;其次,通过在特征提取阶段引入多个不同采样率的空洞卷积,增强小目标的多尺度细节特征提取能力;最后,在多尺度特征融合阶段引入注意力机制,将深层特征进行通道加权后再与浅层特征进行高效融合,增强小目标特征表达能力。实验表明,改进的YOLOv5模型在自制数据集上mAP达到了99.02%,对于小尺寸的无人机目标,具有更好的检测效果。相较于改进前网络,检测速度提高了10.3%,内存开销节约了65%,降低了对设备计算和存储能力的要求,更加有利于无人机检测系统的工程部署和实际应用。

Aiming at the problems of low target detection accuracy for small-sized drones and the deep network has a large number of parameters and a high memory footprint,a drone detection method based on improved YOLOv5 was proposed.Firstly,the number of YOLOv5 multi-scale prediction layers was adjusted,and the redundant network layers was cut,which effectively reduced the amount of network parameters,and improved the speed of drone detection. Secondly,multiple parallel atrous convolutions with different sampling rates were introduced into the feature extraction stage to enhance the ability of multi-scale detail feature extraction of small targets. Finally,the attention mechanism was introduced into the multi-scale feature fusion stage to enhance the feature expression ability of small targets by Fusion of shallow features and deep features were channel-weighted. The experimental results illustrate that the improved YOLOv5 model achieves 99.02% mAP on the self-made data set,and has better detection effect for small-sized drone targets. Compared with the network before the improvement,the detection speed is increased by 10.3% and the memory cost is saved by 65%,and the requirements of computing capabilities and storage capabilities for devices are reduced,and which is more conducive to practical applications and engineering deployment of drone detection systems.