In order to solve the problem of poor discrimination performance caused by high similarity between damage targets in the field of vehicle intelligent damage determination, a vehicle deformation detection method based on improved YOLOv7 was proposed. Firstly, mixed spatial channel attention (MSCA) was added to the backbone network of the model to enable the model to extract more abundant sample features, and thus improve the ability to extract discriminative features. Secondly, the loss function was optimized, and Focal-EIoU was used as a loss function to enhance the quality of anchor frame regression and improve the accuracy of model positioning. Finally, a balance factor regulation method was used for the hard-to-detect samples in deformation damage, which makes the model focus more on the feature capture of hard-to-detect samples, so as to improve the recognition effect of the model on the hard-to-detect categories. The experimental results show that the VDD-YOLO algorithm can improve the average detection accuracy of three types of damage, and the mAP50 reaches 43.9%, which is 3.8% higher than that of the baseline model. In addition, compared with other mainstream advanced case segmentation algorithms, the proposed algorithm has significant advantages in model accuracy and difficult sample detection, and provides a new solution for vehicle intelligent loss determination.