## 1 Introduction  
In modern industrial production, there are numerous potential risks, and the life safety of construction workers is often threatened. According to the analysis of construction site accidents in recent years, fatal accidents caused solely by objects falling from a height account for 24% of all accidents [1]. Additionally, 90% of accidents are attributed to not wearing protective equipment or wearing it irregularly [2]. This underscores the particular importance of detecting safety helmets and sun protective clothing. Up to now, there have been roughly two types of detection methods. The first is completely reliant on manual inspection, and the second is based on artificial intelligence technology [3]. Manual supervision involves using surveillance footage to monitor the on-site situation in real time. While there are no technical difficulties involved, the scene can be chaotic with numerous surveillance screens, leading to oversight and missed observations during manual supervision. Furthermore, it requires a significant amount of manual labor for shift rotations. With the improvement in GPU computing power and the advancement of artificial intelligence over the years, object detection algorithms have also made significant progress. Using object detection algorithms to detect safety helmets and reflective clothing can not only improve detection accuracy but also reduce labor and time costs, while enabling intelligent monitoring and control. The mainstream object detection algorithms can be divided into two major categories: the first category is single-stage detection algorithms, such as R-CNN [4], Fast R-CNN [5], and Faster R-CNN [6]; the second category is referred to as two-stage detection algorithms, including SSD [7] and YOLO [8; 9; 10; 11]. Currently, numerous scholars have undertaken research on the detection of safety helmets and reflective clothing. [PERSON] employed the k-means++ algorithm on the fundamental YOLOv5 model to enhance the size matching accuracy of anchor boxes and incorporated the DWCA attention mechanism into the backbone network. [PERSON] observed that previous studies predominantly relied on prior human knowledge to determine anchor box sizes, which often introduced inaccuracies and overlooked nuances. Consequently, leveraging an algorithmic approach to supersede such prior knowledge yielded superior matching precision. The degree of alignment between anchor boxes and ground truth boxes crucially impacts detection accuracy. In designing the DWCA attention mechanism, [PERSON] incorporated positional information, enabling the network to engage with a broader spatial context. On the custom dataset curated by [PERSON]'s team, the model achieved a remarkable mAP accuracy of 95.9% [12]. However, [PERSON]'s study is not devoid of limitations. Firstly, it neglects considerations regarding model size and computational cost, which are crucial factors for practical deployments. Secondly, the optimization of the backbone network appears relatively straightforward, potentially limiting the model's generalizability across diverse domains. These aspects represent opportunities for future research to further refine and extend [PERSON]'s findings. [PERSON] endeavors to achieve breakthroughs in addressing small object detection challenges. Firstly, he introduces an additional layer of structure to bolster the detection capabilities for small targets. Secondly, he redesigns the pre-selection box sizes utilizing the K-means algorithm (specifically, [PERSON] employed the fundamental K-means algorithm). Lastly, he modifies the loss function, transitioning from CIOU to EIOU. Given that existing public datasets did not meet [PERSON]'s specific requirements, he created a custom dataset, categorizing it into six distinct groups based on the wearing status of safety helmets. This approach, which is relatively uncommon in helmet detection, underscores [PERSON]'s algorithm's enhanced capabilities in detecting small objects and refines the categorization of helmet detection [13].