Purpose-The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestr...Purpose-The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection.This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.Design/methodology/approach-First,to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion,this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity.Second,for the CSPLayer of the PAFPN,a weight gain-based normalization-based attention module(NAM)is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians.Finally,the authors experimentally determined the optimal values for the confidence loss function.Findings-The experimental results show that,compared with the original YOLOX algorithm,the AP of the improved algorithm increased by 2.90%,the Recall increased by 3.57%,and F1 increased by 2%on the pedestrian dataset.Research limitations/implications-The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.Originality/value-The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.展开更多
基金This work was supported by the National Ethnic Affairs Commission of the People’s Republic of China(Training Program for Young and Middle-aged Talents)(No:MZR20007)Hubei Provincinal Science and Technology Major Project of China(No:2020AEA011)+1 种基金Wuhan Science and Technology Plan Applied Basic Frontier Project(No:2020020601012267)the Fundamental Research Funds for the Central Universities,South-Central MinZu University(No:CZQ21026).
文摘Purpose-The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection.This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.Design/methodology/approach-First,to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion,this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity.Second,for the CSPLayer of the PAFPN,a weight gain-based normalization-based attention module(NAM)is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians.Finally,the authors experimentally determined the optimal values for the confidence loss function.Findings-The experimental results show that,compared with the original YOLOX algorithm,the AP of the improved algorithm increased by 2.90%,the Recall increased by 3.57%,and F1 increased by 2%on the pedestrian dataset.Research limitations/implications-The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.Originality/value-The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.
