In order to realize the automatic classification of skin burns of the wounded after suffering a major fire and other disasters and speed up the diagnosis efficiency, a lightweight model BI-YOLOv5 algorithm for skin burn classification was proposed. Replace the Swish activation function to improve the convergence ability and detection efficiency of the model; use the K-means++ algorithm to perform cluster analysis on anchors to enhance the adaptability to targets of different scales; modify the feature extraction network to extract feature information of multiple scales and establish multi-scale features The fusion network improves the utilization rate of the deep feature information by the model and improves the recognition accuracy of small-area burns. The experimental results show that the BI-YOLOv5 algorithm has high accuracy and efficiency in detecting and distinguishing different burn types and environmental disturbances, and the mAP reaches 97.6, which is 8.4 percentage points higher than that of YOLOv5.