基于改进YOLOv5m的轻量化车脸检测方法

为解决车检站车辆检测中需要对车辆前照灯快速准确定位,同时防止车辆代检的问题,建立了一个车脸检测数据集Car-Data。针对车检站场景中车辆检测问题,提出了一种基于YOLOv5m的轻量化车脸检测方法。首先,将原网络的卷积块替换为改进型跨阶段深度可分离卷积块,以减少网络整体的参数量和计算量。其次,提出增强感受野的空间金字塔扩张卷积模块代替YOLOv5m的主干提取网络中的空间金字塔池化模块,从而提升网络的目标检测精度。最后,在颈部特征增强网络中修改上采样方法,并提出上下层特征融合模块,以减少特征信息的损失。在Car-Data数据集上进行的实验结果表明,改进后的算法相较于原YOLOv5m模型大小减少了48%,每秒检测帧数提高了约10帧,且平均检测精度仍提升了2.02%。因此该改进算法可以满足车检站车辆检测场景中车脸检测的需求。

Lightweight car front detection method based on improved YOLOv5m

Aiming at the problem of fast and accurate positioning of car headlights at car inspection stations and preventing car from replacing inspection, a car front detection dataset Car-Data is established. To solve the problems of car detection in the vehicle inspection station scene, a lightweight car front detection algorithm based on YOLOv5m is proposed. First, the convolution block of the original network is replaced by an improved cross stage depth separable convolution block to reduce the parameters and computation of the network as a whole. Then, the spatial pyramid pooling module in the backbone extraction network of YOLOv5m is replaced with the spatial pyramid dilated convolution module of the enhanced receptive field, thereby improving the object detection accuracy of the network. Finally, the upsampling method is modified in the neck feature enhancement network, and an upper and lower layer feature fusion module is proposed to reduce the loss of feature information. The experimental results on the Car-Data show that compared with the original YOLOv5m, the size of the improved algorithm is reduced by 48%, the number of detection frames per second is increased by about 10 frames, and the detection accuracy is still improved by 2.02 percentage points. Therefore, the improved algorithm can meet the needs of car front detection in the car detection scene of the car inspection station.