Semantic Segmentation-based Multi-target and Multi-posture Pedestrian Detection in Substation Environment
Through the jump connection method,the U-Net++deep learning model is constructed.The combination of binary cross-marking and dice coefficients is added as the loss function,then the activation function is improved,and the Res2Net multi-scale backbone architecture is used as the feature extraction network to obtain stronger multi-scale feature extraction ability.Then the semantic segmentation-based detection experiment is carried out on multi-target and multi-posture pedestrians in substation environment.The experimental results show that the pedestrian targets can be detected and segmented accurately under the conditions of very small pedestrian targets,dense pedestrians,non-upright pedestrian gestures and the coexistence of pedestrian multi-scales,with F1-Score index reaching 0.978 and detection efficiency of 0.035 s per image,which are superior to the two selected classical methods.
substationsemantic segmentation-based pedestrian detectionmulti-target and multi-postureU-Net++deep learning model
万方数据
维普
