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.