Monocular Camera-Based Substation Safety Distance Monitoring and Early Warning Method

To ensure the stable operation of substations, staff members are frequently required to enter operational areas. However, due to the lack of proactive monitoring mechanisms, accidental intrusions into live zones often lead to electric shock incidents. To address this issue, this study proposes an intelligent method that integrates keypoint detection, ranging, and early warning into a unified framework. First, a Channel-aware Inception Depthwise Convolution (CIDW) feature extraction module is proposed to enhance the capability of small-object feature extraction. Second, a Context-Dynamic Attention (CDA) feature enhancement module is developed to improve the model’s ability to capture fine-grained details in critical regions. Third, a Context-Enhanced Mechanism (CEM) module is designed to improve the feature pyramid network, thereby strengthening the model’s perception of high-resolution features. To overcome the challenges that monocular cameras cannot directly obtain threedimensional information and that long-distance pixels exhibit nonlinear errors, an innovative Long-distance Error Correction (LEC) model is proposed. By combining an inverse coordinate transformation formula, the LEC model enables high-precision 3D ranging. Experimental results demonstrate that the proposed detector outperforms existing algorithms in terms of detection accuracy, speed, and lightweight design. The ranging accuracy reaches the millimeter level at short distances, with long-distance errors controlled within approximately 5 cm, making it an effective safety monitoring approach for the perception layer of intelligent substation IoT systems.