# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 1 Introduction  
With increasing urbanization, the building industry has experienced substantial growth. However, numerous factors threaten individuals' life safety in the complex environment of construction sites, and research has shown that most of these threats are due to head injuries. Helmets can shield the heads of construction workers, safeguarding their lives and lowering the incidence of risk. In the actual construction site, often due to the lack of safety awareness of construction personnel ignore the helmet wearing.Therefore, the intelligent supervision of the hard helmet wearing status is very important. Due to the emphasis on worker safety, a great deal of research has been undertaken in this field by a large number of experts. Traditional helmet-wearing detection tasks depend primarily on image processing, machine learning, and sensor-sensing-based techniques [1; 2; 3]. Columns include [PERSON] [4] detecting moving things in construction sites using the background modeling approach based on standard image processing and machine learning characterizes the human behavior in the region of interest using the histogram of oriented gradient (HOG). [PERSON] [5] utilized a sensor to put a device for detection on a helmet, enabling the physical detection of helmet use. [PERSON] [6] uses the foreground information extracted from the collected video pictures. The foreground targets were binarized to isolate the moving objects, and the pedestrians were tagged and tracked using scale filtering to separate the travelers from other moving things. Finally, compare the chromaticity distribution of the target with the set helmet color to identify the helmet's color and realize the staff's helmet-wearing detection. All the techniques mentioned above rely on traditional approaches, albeit to varying degrees, to accomplish the effect of helmet detection. In recent years, significant advances have been made in deep learning and intelligent industrial security. In industrial intelligence, deep-learning-based target detection technology is frequently employed. Depending on the existence or absence of a candidate regiongeneration process, the current deep-learning-based target identification algorithms are classified into two-stage and one-stage detection. The stage of target detection is represented by the R-CNN series based on the generation of a candidate frame, the models, such as fast R-CNN [7], faster R-CNN [8], and mask R-CNN [9]. The model based on the candidate region for quadratic correction regression yields highly accurate detection results. Unfortunately, the detection timeliness is middling, making it unsuitable for detection applications with strict timeliness requirements. One-stage target detection models are separated into YOLO [10; 11; 12; 13; 14] and SSD [15] series. Without creating separate candidate frames, our single-stage detection algorithms identify and position-correct these frames directly. The detection speed is increased to satisfy real-time performance requirements, but it does not filter the candidate frames, resulting in a model with very low accuracy. Consequently, we are now optimizing and enhancing the one-stage detection algorithm for helmet detection to achieve the optimal balance between accuracy and speed for helmet detection. In this paper, we examine the challenges of erroneous detection caused by a hand-held helmet task, a long distance and a tiny target, and a low detection rate caused by dense and severe occlusion, by enhancing the YOLOX-s method and verifying it using a custom-built helmet dataset [16]. We have mainly made the following improvements on YOLOX-s: (1) To address the issue of challenging feature extraction in the target and increase detection accuracy, the HorNet module is utilized to replace the Res8 module of the backbone network. (2) The original FPN structure is replaced with BiFPN to improve feature fusion while minimizing feature loss, resolving the issue of low detection accuracy under dense occlusion.