In practical scenarios, vehicle targets are often obstructed by other vehicles, buildings, and other objects, and the background may also be very complex. To ensure detection accuracy, a vehicle multi-target detection method combining convolutional neural networks and LiDAR is proposed. Using LiDAR to capture vehicle target images, the collected vehicle driving images are divided into two sides based on their lane line characteristics. The area within the lane line is used as the initial region of interest (ROI) for vehicle multi-target detection. In the ROI, the vehicle bottom shadow hypothesis region segmentation method is used to obtain the hypothesis region for vehicle detection targets. On the basis of the original convolutional neural network, further optimization is carried out to design a deformable convolutional neural network (DF-R-CNN) model. The assumed region obtained is used as the candidate region for vehicle multi-target detection required by the network model, and accurate detection of vehicle multi-targets is achieved through this model. The experimental results show that the highest recall rate of the proposed method reaches 85%, and the lowest loss function value is about 1.8, indicating that it has high detection accuracy and effectiveness.