Part-aware based pedestrian detection method for driving scenes
Pedestrian detection in driving scenarios faces challenges such as complex environments,dense pedestrian population and large scale span.This paper proposes an end-to-end pedestrian detection method for intelligent driving scenarios.To address the scale issue and reduce information loss caused by directly adding features from the feature pyramid,a Bidirectional Feature Enhancement Module(BFEM)is introduced,which enhances the information contained in each layer of features through concatenated fusion and bi-directional channels.To address the problem of insufficient perception of the detector in pedestrian occlusion scenes,this paper adds the Embedding-based Attention Part-aware Module(EAPM)to the detector,which uses task-aware attention-enhancing feature foreground features,while adding visibility loss for human parts as a way to enhance the model's perceptual experience of the human structure.In addition,this paper improved the idea of task perceptual attention combined with spatial grouping,enhanced sub-feature information,reduced noise interference,and thus enhanced the classification ability of the detector.The proposed method is evaluated on the CrowdHuman and Citypersons dataset,and the experimental results demonstrate its effectiveness.Compared with the baseline,it achieves significant improvements of 2.39％in AP,2.21％ in Recall,and 3.08％ in R-2M,achieving 91.55％ AP,89.88％ Recall,and 43.90％ R-2M.On Citypersons dataset,it achieves a result of 44.4 R-2M.
万方数据
维普
