基于混合注意力机制与C2f的行人检测算法研究OA

Aiming at the problems of missing detection and false detection of small target pedestrians with small scale and dense pedestrians,this paper proposes a pedestrian detection algorithm with mixed attention mechanism and C2f module based on YoloX algorithm.In this algo-rithm,firstly,the BAM module and the C2f module are fused to effectively enhance the characteristics of pedestrians,reduce the amount of calculation,and improve the detection speed.Secondly,the attention mechanism is used to guide the network to pay attention to pedestrian tar-gets,and the characteristic information of pedestrian targets has been further strengthened.Finally,experimental analysis is carried out on the Crowd Human dataset,when the IoU threshold is set to 0.5,the small-scale pedestrian detection accuracy is 21.1%,the mesoscale pedestrian detection accuracy is 47.3%,the large-scale pedestrian detection accuracy is 64.7%,the total pedestrian target detection accuracy is 73.2%,and the detection speed is 24.3 frames per second.Experimental results show that the pedestrian detection algorithm in this paper effectively improves the detection accuracy and detection speed of pedestrian targets,and has good detection performance for pedestrian targets.