Human skeleton key points detection model of substation operators based on mask initialization

Bo Xu; Wenping Nie; Meng Xue

doi:10.1117/12.2684972

19 October 2023 Human skeleton key points detection model of substation operators based on mask initialization

Bo Xu, Wenping Nie, Meng Xue

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092B (2023) https://doi.org/10.1117/12.2684972
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Substation personnel behavior detection is mainly based on the two-dimensional image taken by the camera for discrimination. The substation has the characteristics of complex environment and multiple types of personnel operations, which leads to the low accuracy of two-dimensional image of personnel behavior discrimination. To solve this problem, this paper studies a human skeleton point detection model based on mask initialization. First, the operator image in the substation is processed by using the joint detection and segmentation network Mask R-CNN to obtain the boundary frame and mask of the human target, and then the target area is detected from bottom to top. Experiments show that this method not only reduces the impact of complex environment and job types on the detection accuracy, but also greatly reduces the invalid convolution operations, thus improving the detection speed.

Citation Download Citation

Bo Xu, Wenping Nie, and Meng Xue "Human skeleton key points detection model of substation operators based on mask initialization", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092B (19 October 2023); https://doi.org/10.1117/12.2684972

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