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.
关键词
变电站/行人语义分割检测/多目标多姿态/U-Net++深度学习模型
Key words
substation/semantic segmentation-based pedestrian detection/multi-target and multi-posture/U-Net++deep learning model
维普
万方数据