# Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices
## 2 Related Works  
The YOLO family of algorithms, known for their efficiency and simplicity, was first introduced by [PERSON] et al. in 2015. In the years that followed, [PERSON] et al. released YOLOv2 and YOLOv3 algorithms, which further reduced network complexity and improved detection speed compared with two-stage algorithms [7]. After [PERSON] withdrew from the field of computer vision, [PERSON] released YOLOv5 in 2020, which has since been updated to version 6.2. YOLOv5 is composed of a backbone feature extraction module, a neck feature fusion module, and a head detection module, as illustrated in Figure 1. The algorithm incorporates five different scales of n, s, m, l, and x, with larger scales delivering higher detection accuracy but slower real-time performance. However, the network structure of the models of different scales remains consistent, differing only in the number of partial layers, and is represented uniformly as \"\(\times\)n\" in the figure. YOLOv5 uses CSPDarknet53 as its backbone network, which includes the Cross Stage Partial (CSP) structure [8]. The CSP structure integrates gradient changes in the feature map, reducing the problem of repeating gradient information in the backbone network. Moreover, YOLOv5 utilizes a bottleneck structure with residual connections in the backbone network to prevent network degradation due to gradient disappearance, and a bottleneck structure without residual connections in the feature fusion layer to reduce computational effort. Additionally, [PERSON] employs a modified Spatial Pooling Pyramid Fast (SPPF) structure in place of the original SPP [9]. The modified SPPF achieves the same computational results as the original parallel MaxPool layers of three different sizes by serializing multiple MaxPool layers of the same size, significantly reducing computational time.  
In addition to YOLOv5, various target detection methods have been proposed for infrared scenes by researchers. [PERSON] et al. [10] proposed the YOLO-FIRI model, an infrared image area-free target detector based on YOLOv5. They achieved good infrared target detection performance by improving the CSP structure and introducing multiple detection heads. [PERSON] et al. [11] improved the feature extraction capability by using dense connection blocks based on YOLOv5, and improved the detection accuracy by adding a channel focus mechanism and modifying the loss function. [PERSON] et al. [12] proposed TIRNet, which adopted VGG as the feature extractor and used a continuous information fusion strategy to obtain more accurate and smoother detection results. [PERSON] et al. [13] designed a dense nested interactive module to achieve progressive interaction among high-level and low-level features. [PERSON] et al. [14] utilized multiscale mosaic data augmentation to enhance the diversity of objects and proposed a parameter-free attention mechanism to enhance features. Although these methods can be applied to IR target detection, they have some drawbacks. For instance, striped targets in IR road scenes require a more reasonable combination of striped convolution and traditional convolution to extract features. The bounding box loss function of the algorithm needs to be more accurately adapted to the boundary regression of targets in IR images, and the model needs to be more lightweight to be suitable for practical edge devices. Therefore, the method in this paper focuses on improving the above shortcomings.