In order to improve the accuracy of personnel intrusion detection in the work area, this paper proposes a method for personnel intrusion detection in high-voltage field work areas based on infrared grating technology. This method utilizes infrared grating technology to collect visual information of personnel images, and analyzes structured similarity features, combined with supervised comparative learning and backbone feature extraction methods, to achieve feature segmentation and reconstruction processing. At the same time, the edge bounding contour features of personnel are obtained through ambiguity analysis to achieve recognition of personnel intrusion behavior. After experimental verification, the results show that this method has high accuracy in intrusion detection, with an average false alarm rate (FPR), false alarm rate (FNR), and false alarm rate (FAR) of 2.69‰, 3.13‰, and 3.21‰, respectively, and a detection rate of 16.25 frames/s.