Lightweight Airborne Vision Abnormal Behavior Detection Algorithm Based on Dual-Path Feature Optimization

Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior targets is strengthened, and the computational efficiency of the model is improved. Finally, we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset. The results show that com-pared with the baseline model, the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%, and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision. The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model, indicating the effectiveness of the improved method in this paper.