## 1 Introduction
However, it is worth noting that these six categories, while meticulous, might introduce complexity in real-world applications, potentially resulting in some detection outcomes with less distinct boundaries. Furthermore, the algorithm still exhibits lower accuracy in distinguishing between categories with subtle differences. [PERSON]'s algorithm presents a substantial improvement over YOLOv3 by introducing several key enhancements. Firstly, the integration of a larger input layer significantly enlarges the perceptual field of the feature map. Secondly, the adjustment of candidate box sizes is a strategic move aimed at optimizing the detection of small-sized objects. Remarkably, [PERSON]'s algorithm maintains a satisfactory frames per second (FPS) while achieving a superior mean average precision (mAP) at the same resolution compared to YOLOv3. This dual performance ensures real-time monitoring capabilities without compromising on accuracy. However, it is acknowledged that the enhanced algorithm's increased model size poses challenges for practical deployment, particularly in resource-constrained environments. Additionally, the reliance on the relatively outdated YOLOv3 framework as a starting point limits the algorithm's potential for further advancements [14]. [PERSON]'s algorithm builds upon enhancements to YOLOv5. Initially, it employs Ghost modules to replace certain convolutional and C3 modules. Subsequently, it incorporates a CA attention mechanism into the backbone network, and ultimately replaces the C3 module in the neck layer with C3 CBAM. On a custom-built dataset, the algorithm achieved a mAP accuracy of 93.6%, reducing computation by 41.7% and the model size by 37.3%. Optimizations have focused on lightweighting. Although the algorithm is primarily aimedat lightweighting, there has been no concomitant improvement in accuracy. Thus, future enhancements and optimizations are required to boost accuracy [15]. Although these methods have increased the accuracy of the object detection algorithm to a certain extent, the [EMAIL_ADDRESS] has not reached 95%, and the [EMAIL_ADDRESS]:0.95 fluctuates around 60%. This paper argues that this cannot meet our requirements for model accuracy. Furthermore, most researchers have focused solely on optimizing the detection of safety helmets, neglecting the detection of reflective clothing, thereby limiting the application scenarios. In response to this existing issue, this paper proposes the SDCB-YOLOv8 model, which achieves higher accuracy and more comprehensive detection capabilities. Firstly, this paper introduces the SE attention mechanism to optimize the allocation of computational resources, aiming to achieve higher accuracy with the same computational power consumption. Secondly, the loss function was switched to DIOU to reduce computation. Additionally, a more lightweight upsampling operator was used, addressing the issue of simplistic sampling steps in traditional algorithms while maintaining lightweight characteristics. Finally, a BiFPN was adopted to replace the original FPN, offering more targeted feature fusion steps. The first two sections of Section 2 in this paper introduce the dataset and the experimental environment used. Sections 2.3-2.8 of Section 2 detail the modification process and objectives of the model. Section 3 introduces the evaluation metrics of the model and compares the models using these metrics. The fourth part introduces the achievements of this paper and the direction for future improvements.