## 1 Introduction
The other is an end-to-end single-stage strategy, including YOLO series [16; 17; 18], SSD [19], RetinaNet [20], etc., which tries to solve the problem of object boundary box localization using regression methods and completes object detection and localization simultaneously without the need for additional region proposal steps. It significantly surpasses the two-stage model in speed and can provide real-time detection, making it particularly well suited for scenarios with stringent real-time requirements, like running on edge devices [21]. Hence, more and more scholars focused on single-stage detection algorithms and developing more robust and efficient network models to design detectors. This trend indicates that the demand for accuracy, reliability, and efficiency is constantly increasing, which also means that object detection algorithms need to be continuously optimized to meet these new requirements. The one-stage object detection model generally comprises three key components: a fundamental backbone, a feature-enhanced fusion network in the neck, and a parallel head. These three components collaboratively capture elements from the feature map, ensuring precise object detection [22]. The backbone captures the encoding of features in the input image at different levels. The enhanced feature fusion network acts as a bridge between the backbone network and the head to further refine features. Through upsampling and downsampling operations, the features of diverse dimensions are obtained, and features of diverse scales are fused and compressed to optimize the overall performance of object detection. The prediction network performs object classification and regression of bounding box positions to generate object detection results employing enhanced features. This means that the quality of the feature extraction plays a pivotal role in determining the outcome of the ultimate prediction. [PERSON] et al. [23] used lightweight PConv to replace the original convolutional layer to reduce parameters and introduced coordinate attention in the backbone to enhance the ability to position the target. [PERSON] et al. [24] introduced BIFPNto improve the fusion network so that it could capture and utilize multiscale features more effectively, thus enhancing the performance of the object detector. [PERSON] et al. (2020) proposed the use of a radius-aware loss function to consider the radius between the prediction result and the target to better evaluate the accuracy of the prediction. Regarding architecture, the head can be categorized into two main methods: anchor-based and anchor-free detection approaches ([PERSON] et al., 2020). Among them, the former has stable training, accurate target position regression, and is advantageous when dealing with dense target distribution. In contrast, the anchor-free detection method has higher detection speed, lower computational complexity, but may behave unstably when the target is occluded. Since there is no predefined anchor box, the algorithm may have difficulty in handling cases of occluded targets, resulting in decreased detection accuracy. It is worth mentioning that the current one-stage model YOLOv5, which achieves a flawless blend of accuracy and pace, uses an anchor-based detection method. To ensure the stability and adaptability of an infrared detection model in complex scenes, this study selected the lightweight YOLOv5 framework as the benchmark model for infrared detection. This study identified three areas where the model needs improvement: (1) Due to the complexity of the real environment and the very weak characteristics of infrared targets, there are still problems such as weak anti-interference ability in the face of multi-scale targets. For example, in the verification of some occluded targets, the model cannot accurately identify them, resulting in reduced accuracy.