An improved YOLOv3 algorithm that detects target vehicles is proposed to address the problems of low detection accuracy of small targets and poor robustness of systems in target vehicle detection. First, the proposed algorithm introduces the dilated convolution into the downsampling layer of the YOLOv3 algorithm, improving the resolution of the feature maps and detection effect of small targets. Second, to address the problem of small target recognition in vehicle images, the proposed algorithm increases the three detection scales of YOLOv3 to four in addition to connecting and fusing the information with different scales, and the improved feature pyramid structure further improves small target detection. Finally, using Complete IoU (CIoU) as the loss function makes the target frame regression more stable, and there is no divergence in training. The KITTI dataset test results show that the improved YOLOv3 algorithm can achieve high detection accuracy. The proposed algorithm improves the average detection accuracy by 4.6%, and the detection rate is approximately 44.1 frame/s. On the premise of improving the accuracy, the proposed algorithm maintains a high detection rate.