# Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices
## 1 Introduction  
Visible light images are commonly used in target detection due to their high resolution, definition, and detailed visual information that is easily interpreted by the human eye. However, these images are sensitive to external factors such as weather and lighting conditions, which can reduce image quality and negatively impact target detection accuracy. This is where infrared imaging technology plays a crucial role. By overcoming these limitations, infrared imaging allows for image acquisition under diverse lighting and weather conditions, including foggy days and nighttime scenarios. As a result, this technology has been widely adopted in various fields, such as autonomous driving, security monitoring, and remote sensing, and offers a broader range of use cases than visible images. Therefore, the importance of infrared imaging technology in enabling efficient and reliable target detection cannot be overstated. Traditional infrared target detection techniques [1; 2; 3] are mainly model-based methods, such as template matching, threshold segmentation, and the Hausdorff metric. However, with the development of deep learning, target detection techniques based on convolutional neural networks have emerged in recent years. These methods are primarily divided into two-stage algorithms (e.g., Faster-RCNN [4]) and single-stage algorithms (e.g., SSD [5] and YOLO [6]). The single-stage algorithm is designed to achieve a balance between detection speed and accuracy, resulting in a significant improvement in detection speed while maintaining accuracy compared with the two-stage algorithm. Consequently, the YOLO series has become a widely used representative of the single-stage algorithm. Among the mainstream YOLO algorithms, YOLOv5 (version 6.2) has achieved significant improvement in both detection accuracy and speed compared with its predecessor by using Mosaic data enhancement, C3 modules, and an improved SPPF module. Although YOLOv5 performs well on visible images, it encounters several challenges when applied to infrared detection. The first major issue is that infrared images suffer from poor contrast, high noise, and blurred imaging, leading to the loss of crucial target features during deep convolutional networkprocessing. Moreover, since infrared images lack color information, and the difference between target and background features is minimal, it becomes challenging for deep convolutional neural networks to distinguish useful information from irrelevant data, reducing detection accuracy. Another significant challenge is that embedded devices commonly used in autonomous driving and security monitoring fields have limited computing power, storage space, and power consumption, which makes deploying large target detection models such as YOLOv5 difficult. Additionally, these fields require real-time detection, and collecting data on edge devices and sending them to the server for detection and analysis can lead to network latency and communication congestion problems in widely distributed areas. The challenges mentioned above necessitate lightweight infrared target detection on edge devices. In summary, this paper proposes a solution to these issues by introducing Edge-YOLO, an infrared target detection algorithm that utilizes lightweight networks and attention mechanisms specifically designed for edge devices. The primary enhancements of this algorithm can be summarized as follows:  
1. The bounding box loss function was redesigned, and a loss function with a power hyperparameter \(\alpha\) was made to accelerate the convergence of the loss function and solve the uncertainty of the aspect ratio in CIoU;
2. A lightweight content-aware up-sampling operator was adopted, which can obtain a larger perceptual field than the original nearest-neighbor up-sampling method, while only introducing a small number of parameters and computational cost;
3.