# EGS-YOLO: A Fast and Reliable Safety Helmet Detection Method Modified Based on YOLOv7
## 1 Introduction  
Building construction, road construction, and other types of construction are high-risk operations. During construction activities, improper operations and construction personnel failing to follow prescribed measures can cause damage to their life and health. Among these risks, improper wearing of protective devices is a major cause of serious consequences. According to statistics, there were 689 municipal engineering production safety accidents in China in 2020, including 83 incidents of physical strikes. These statistics indicate that it is essential to take safety protection measures at construction sites. Head injuries during construction are extremely dangerous. Helmets can absorb external impact forces and prevent object penetration, making them essential tools for reducing the incidence of such injuries. However, according to a survey by the Bureau of Labor Statistics, 84 percent of workers who suffered head injuries were not wearing helmets [1]. Therefore, monitoring the use of helmets by construction workers is crucial. Manual monitoring is costly, time-consuming, and inefficient. In contrast, automatic detection and supervision, facilitated by computer vision, the Internet, and other technologies, have emerged as new developmental trends. This approach offers both cost savings and significantly increased security. Research into construction site security protection, closely integrated with deep learning technology, is gradually becoming a hotspot among scholars. This research provides a new model for production safety management and can offer technical support and references for future smart construction sites, intelligent manufacturing, and other fields. In previous studies, safety helmet detection primarily involved traditional methods and deep learning-based approaches. Initially, [PERSON] et al. [2] proposed the employment of RFID (Radio Frequency Identification) to check workers' compliance with safety wear. However, RFID is limited in scope, as it can only confirm the helmet's proximity to the worker, not whether it is being worn correctly. [PERSON] et al. [3] put forward a cyber-physical system (CPS) to detect workers wearing PPE (personal protective equipment) in real-time, but this system could not determine whether workers were wearing helmets correctly. [PERSON] et al. [4] utilized a positioning system for virtual building technology where a pressure sensor in the helmet recorded relevant information and relayed it via Bluetooth. However, this method was ineffective over long distances. [PERSON] et al. [5] employed a visual background difference algorithm for worker identification and used principal component analysis for feature dimensionality reduction. They ultimately used the Bayesian optimization SVM (Support Vector Machine) model to identify hard hats, but the accuracy was not high. Traditional methods for safety helmet detection have low accuracy, poor generalization, and low efficiency. Consequently, deep learning-based object detection techniques are now the preferred approach. [PERSON] et al. [6] implemented helmet detection using Faster R-CNN for targets in the far field and scenes with various construction site backgrounds, achieving excellent detection results on the collected dataset. Although the Faster R-CNN model has higher detection accuracy, it is poor in real-time and has a large model size. [PERSON] et al. [7] addressed the problem of low helmet detection accuracy; they improved YOLOv5 by increasing the detection head, feature fusion, and attention mechanism, which enhanced the model's detection accuracy in complex backgrounds, and the average accuracy was improved by 2.6%. However, the inference time of the model is not satisfactory enough, and there is a large room for improvement. [PERSON] et al. [8] conducted extensive experimental research on helmet detection systems and found that YOLOv5X performs excellently in low-light conditions with high precision. However, the YOLOv5X model is large, and its inference time requires further investigation.