# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 2 Related Work  
Because the first-stage target detection algorithm has the advantages of high detection accuracy and speed, many researchers have conducted a lot of research and optimization based on the first-stage target detection model for helmet-wearing detection tasks. [PERSON] et al. [17] used the cross-stage partial network (CSPNet) to improve the backbone network of Darknet53 based on the YOLOv3 model with a spatial pyramid set (SPP) structure. They also enhanced the multiscale prediction network with top-down and bottom-up feature fusion strategies to achieve feature enhancement. The improved model detection accuracy and speed increased by 28% and 6 fps/s, respectively, compared with the original YOLOv3. [PERSON] et al. [18] used the improved YOLOv3 algorithm to output the predicted anchor box of the target object for pixel feature statistics and output the confidence level, followed by increasing the fourth feature map scale, enhancing the loss function, and combining the image processing pixel feature to detect whether or not the helmet is worn accurately. The detection speed and precision of the enhanced detection algorithm for the helmet detection task have been enhanced in comparison with the prior method. [PERSON] et al. [19] improved the model's accuracy and speed by swapping out the tedious overlap of many convolution modules in the original YOLOv4 feature pyramid for cross-stage hierarchical modules. Changing the linear transformation of the YOLOv4 feature layer output and anchor improves detection performance for helmet identification of tiny targets. [PERSON] et al. [20] updated helmet wearing recognition method based on YOLOv5 to collect more efficient information. MCA attention mechanism is introduced to the feature extraction network, and the improved algorithm is pruned to compress the model. [PERSON] et al. [21] added a convolutional attention module to the feature fusion network in the YOLOv5 backbone network by employing a deformable convolutional net instead of the conventional convolution and replacing the original network's generalized cross-joint loss function with distance cross-joint loss. [PERSON] et al. [22] proposed a method for helmet detection to enhance the YOLOX-s network. The number of training sets is increased by merging the mosaic approach with online complex sample mining (OHEM); a branch attention module is introduced to the prediction side of the model (prediction), and a novel cosine annealing algorithm is presented to minimize the model's convergence time. The improved helmet detection method improves the mAP, accuracy, and recall rate of helmet detection compared with those before the improvement.  
Most of the scholars above mainly optimize the detection speed and accuracy of the model, but they do not address the actual problems faced in the task of helmet-wearing detection, so this study addresses the four issues of hand-held helmets prone to wrong inspection, the long-range small target phenomenon, the severe obstruction problem, and the crowding phenomenon based on YOLOX-s. The problem is solved and optimized.