基于YOLO与动态正交注意力的暴力行为检测算法OA

Aiming at the problems of inadequate spatio-temporal information modeling and poor robustness in complex scenes in existing violence detection methods based on Deep Learning,a violence detection algorithm based on YOLO and hybrid Attention Mechanism is proposed.The algorithm integrates orthogonal channel attention OrthoNets into the C2f module in YOLO backbone network,and uses randomly initialized orthogonal filters to compress feature space information,ensuring feature diversity for subsequent extraction.The channel space mixed attention module CBAM is integrated into the Neck end to enhance the network feature representation capability.The experimental results demonstrate that the algorithm achieves accuracy,recall,and average precision of 91.25%,95.6%,and 87.5%,respectively,on the RWF-2000 dataset characterized by diverse content and complex environments.It outperforms the most advanced algorithms of the same kind at present in both accuracy metrics and robustness,fully validating its effectiveness.