## 2 Related Work  
PPE significantly mitigates safety hazards for substation workers [7]. This protective equipment typically includes safety helmets, insulated gloves, and operating rods [8]. The algorithm for early detection of PPE mainly relied on sensor networks. Sensor technology detects PPE by installing sensors and analyzing signals. For instance, [PERSON] et al. [9] employed RFID sensors to monitor how workers wear protective gear. However, this system is limited by its numerous hardware components and restricted coverage area. [PERSON] et al. [10] used scanners to identify RFID tags on PPE components, verifying the correct usage by workers. [PERSON] et al. [11] used pressure sensors and Real-Time Location Systems (RTLS) to check if workers were following safety rules, like wearing helmets. [PERSON] et al. [12] developed a drone-based system equipped with RFID detectors to identify tags on ground materials. Yet, this system's effectiveness is significantly hindered by the environment. It often results in delayed and imprecise feedback, and its deployment is complex. [PERSON] et al. [13] created an IoT-based helmet detection system incorporating infrared and thermal sensors. However, its utility is limited by incomplete internet coverage across construction sites. Moreover, [PERSON] et al. [14] developed an RFID-based system for PPE monitoring at workplace entrances, which notably lacks the capability for continuous surveillance. While sensor-based methods can identify PPE usage, their deployment and maintenance are expensive and complex. Furthermore, environmental factors and specific target categories restrict the accuracy of such detection methods. Consequently, real-time and efficient computer vision approaches have gained widespread adoption. Early computer vision relies on machine learning technology. Machine learning enables computers to learn from data and make decisions and predictions. For example, Support Vector Machines (SVMs) effectively handle nonlinear features by finding the optimal hyperplane in high-dimensional space. [PERSON] et al. [15] proposed a safety helmet recognition method combining color space and multilayer SVM. Similarly, [PERSON] et al. [16] developed a multi-feature visual anti-occlusion framework. However, SVM is sensitive to lighting and background, limiting its detection capability in complex scenes. The K-Nearest Neighbors (KNNs) algorithm classifies based on the nearest neighbor labels. [PERSON] et al. [17] used the KNN to identify moving objects in videos, then classified pedestrians and safety helmets with Convolutional Neural Network (CNN). However, KNN is easily affected by data noise. Bayesian algorithms use conditional probabilities for classification purposes. [PERSON] et al. [18] developed a Bayesian Network (BN) model to identify and mitigate factors contributing to accident risks. Nonetheless, Bayesian methods struggle with complex or nonlinear data relationships. K-means clustering is another approach, categorizing data into K-distinct clusters to minimize background noise and interference. [PERSON] et al. [19] used fuzzy sets and K-means to evaluate factors of PPE non-compliance. However, K-means is limited in processing features like color and texture, which are unsuitable for complex scenes. Despite its widespread use, the combination of image processing and machine learning often requires manually extracting complex features. This can limit the analysis of large datasets and burden computational resources. Due to these limitations, especially in detecting PPE, deep learning methods in computer vision for object detection have become more popular recently. Deep learning techniques are categorized into single-stage and two-stage methods. Two-stage methods incorporate RPN and merge candidate region generation with object detection. This blending improves both the speed and precision of detection. For example, [PERSON] et al. [20] used a two-stage method with R-CNN for detecting PPE, achieving a mean average precision (mAP) of 96%. [PERSON] et al.