## 1 Introduction  
In the context of power construction, safety accidents resulting from unsafe behaviors among staff are common occurrences. In complex construction site scenarios, hidden safety hazards may arise when workers neglect to wear safety equipment, which poses a serious threat to their safety and lives [1]. Compliance with safety regulations, including the proper usage of safety helmets, safety belts, work clothes, and safety gloves, is of paramount importance in safeguarding the well-being of individuals engaged in construction work. Despite the existence of safety regulations, enforcement among workers often falls short, which poses challenges in ensuring safety at construction sites. Consequently, power grid enterprises face difficulties in monitoring and verifying workers' compliance with these regulations. Identifying and warning workers about non-compliance is crucial to maintaining safety on construction sites. In recent years, video surveillance cameras have been widely adopted in substation construction safety management systems [2]. However, traditional detection methods that rely on manual labor exhibit several drawbacks, such as high rates of missed detection, low efficiency, labor-intensiveness, and low reliability [3]. These drawbacks are largely attributed to the complex backgrounds and terrains of Yunnan province, where substation construction takes place. To address the challenges of slow transmission speed and lack of timely warning for violations, real-time detection of compliance wearing is necessary to prevent accidents. Deep learning-based object detection algorithms have been increasingly applied in recent years to replace manual monitoring of on-site safety regulations. These algorithms offer a more reliable and efficient way to ensure the safe operation of the power grid [4]. Furthermore, recent advances in drone and camera phone technology have made it possible to capture video sequences with moving cameras for detecting and tracking of moving objects [5]. These object detection algorithms rely on cloud platforms with substantial computing power or high-performance GPU computer clusters to achieve superior detection rates. However, GPU chips have limitations in terms of server power consumption, size, and portability, making them unsuitable for front-end scenarios. Therefore, low-cost edge devices offer a viable solution by simplifying hardware complexity and providing efficient and effective power construction safety detection in real-world settings. At a construction site that requires significant power, video acquisition equipment is connected to the substation's intelligent management system via a high-speed 4G or 5G network. This advanced technology allows the substation's remote safety construction management and control system center to capture live video feeds and closely monitor every aspect of the construction process. However, environmental constraints such as incomplete 4G or 5G signal coverage and signal delays in certain field construction environments must be taken into consideration. For instance, Yunnan Province exhibits a complex topography, with mountains and plateaus comprising over 90% of its landscape. As a result, signal delays can easily trigger electric shock hazards and high-altitude accidents. Furthermore, traditional safety measures primarily focus on post-incident actions rather than proactive prevention. Therefore, real-time violation detection is crucial in reducing casualties. In addition, given the special environmental requirements, the model developed in this study is deployed based on edge devices. Edge devices provide the necessary localized processing for time-sensitive operations and can provide local versions of cloud services to maintain system functionality in the event of a cloud server disconnection. Object detection is a crucial task in computer vision, and numerous methods and algorithms have been proposed to address this issue.