To effectively address the issues of missed and false detections caused by inter-class feature similarity and intra-class diversity in vehicle damage detection, a vehicle damage detection method incorporating explicit detail feature reasoning was proposed. Firstly, the explicit detail edge features of vehicle damage are introduced into WIoU (wise intersection over union) framework, forming V-WIoU (vehicle damage WIoU) based on vehicle damage entropy. V-WIoU was designed to learn explicit edge curvatures of different damage types. Subsequently, curvature distribution entropy was calculated to quantify the complexity of mask boundaries. This entropy metric was integrated into the outlier term of WIoU, which enables effective differentiation of learning difficulties among various damage categories and enhances the feature extraction capability of the model. Secondly, a detail feature capture module was developed to efficiently extract explicit detail textures and spatial explicit feature information of vehicles. On this basis, a detail feature logical correction module was further proposed and embedded into the vehicle damage detection model. This module introduces logical correction rules to impose reasoning constraints on the distribution of explicit features, thereby effectively alleviating false detection issues in damage detection tasks. Experiments are conducted on a self-constructed dataset containing 10 types of vehicle damages, and the results demonstrate that the proposed model outperforms other advanced instance segmentation models in terms of overall detection and segmentation performance. Compared with the baseline model, the proposed method increases the accuracy of object bounding box detection and mask segmentation by 6.8% and 7.6%, respectively. Ablation experiments further verify the significant contributions of each proposed module to the improvement of detection performance. In conclusion, through the synergistic effects of V-WIoU, the detail feature capture module, and the detail feature logical correction module, the proposed model effectively mitigates the detection challenges caused by insufficient feature extraction, inter-class similarity, and intra-class diversity, thus significantly enhancing the accuracy and robustness of vehicle damage detection.