Considering the problem of missed pedestrian detection in dense pedestrian images， a multi-branch non-anchor frame network （MBAN） detection method is proposed to detect various posture changes and serious human occlusion in multi-person traffic scenes， such as streets. First， a multi-branch network structure is added after model backbone network detection to detect the local features of multiple key areas with pedestrians. Subsequently， the distance loss function between key areas is designed to guide the branch network to differentially learn the local detection position of pedestrians. Thereafter， four up-sampling blocks are added to the tail of the ResNet50 network to form an hourglass structure， thereby improving the branch network’s ability to understand the spatial information of local features of pedestrians. Finally， a local feature selection network is designed to adaptively suppress the non-optimal values of the multi-branch output and eliminate the redundant feature box in prediction. In the experimental results， the mAP， F1， Prec， and Recall values of the MBAN method for pedestrian detection in multi-person scenes reached 85.22%， 0.87， 80.07%， and 94.39%， respectively. Therefore， this method is effective in detecting pedestrians in dense crowds and has higher recall rate compared with other pedestrian detection algorithms.