# Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW
## 1 Introduction  
The construction industry is known to have a significantly higher accident rate than most other industries [1]. Within the construction industry, head injuries result in the highest disability rate, making it crucial to prioritize measures that reduce the likelihood of such injuries. One effective measure to enhance workplace safety is the mandatory utilization of appropriate protective equipment, such as helmets. These helmets serve to buffer and disperse the kinetic energy from falling objects upon impact, thereby mitigating the risk of direct head injuries. To achieve this goal, enhancing the supervision of helmet usage can be instrumental in enhancing the protection of construction workers and reducing the likelihood of workplace mishaps. During the initial stages of construction site supervision, helmet-wearing compliance is typically assessed through manual observation, which involves inspecting individual workers to determine whether they are wearing helmets. However, due to the high mobility of construction personnel, it is often challenging for inspectors to fully monitor the safety compliance of every unit. This approach may lead to increased labor costs and could potentially heighten the risk of safety hazards. Therefore, the development of advanced methods to detect helmet-wearing compliance among construction personnel is crucial and holds significant potential for improving overall safety and reducing workplace accidents. Machine learning (ML) techniques are increasingly being used to monitor safety equipment in the workplace, especially the use of helmets. Currently, there are two main algorithms used for this kind of monitoring: an image recognition method based ontraditional ML and an image recognition algorithm based on deep learning (DL). Both methods are dedicated to improving the safety standards of working environments and effectively preventing safety accidents. The algorithms commonly used to monitor the wearing of safety helmets (SH), on the other hand, focus on identification by analyzing color and shape characteristics. [PERSON] and [PERSON] [2] proposed a method for face localization through skin color by intercepting the square region of the face and using the extracted Hu moments as features, training these Hu moment features using SVM, and finally constructing a classifier capable of recognizing helmets. [PERSON] et al. [3] used Haar-like features to detect faces, an edge detection algorithm to find helmet contour features, and an over fused skin color detection technique and support vector machine (SVM) to develop a hybrid approach for safety helmet recognition. However, there are limitations in this method, as the detection is not effective when the worker is not facing the camera or the contrast between helmet and background is low, and the rapid movement of the worker may also lead to detection failure. [PERSON] et al. [4] proposed the use of histogram for orientation gradient (HOG) features in the pedestrian detection phase and color histograms in the HD phase to determine whether a helmet is worn or not by using the spatial matching relationship between a human and a helmet. [PERSON] et al. [5] proposed a method for foreground detection using a hybrid gaussian model followed by processing regarding the connected domain, which uses a model-based technique to recognize the human body and combines SIFT features and color statistical features to detect helmets. [PERSON] et al. [6] utilized discrete cosine transform (DCT) with an HOG algorithm to extract frequency domain information from the image and used a support vector machine (SVM) classifier to distinguish between human and non-human targets. FE combines color and circular Hough transform (CHT) to identify helmets with specific colors and circular contours. Although these traditional ML-HD methods have some advantages in terms of detection speed, these methods rely on manually designing features and training classifiers for a specific object.