# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 1 Introduction  
The images captured using UAVs contain numerous small targets related to power transmission lines. These small objects, such as bolts, insulators, and various fittings, are often critical and vulnerable components that ensure the safe operation of power transmission and distribution lines. There are two ways of defining small targets in power line images. One approach is the relative size definition, where targets with an area less than 0.12% of the total image area are considered to be small targets (assuming a default input image size of 640 \(\times\) 640). The other approach is the absolute size definition. For instance, in COCO data, targets smaller than 32 \(\times\) 32 pixels are considered small targets [1]. Small targets are typically objects that are relatively small compared to the original image size. In these transmission line images, there are many small components, such as insulators capable of withstanding voltage and mechanical stress, as well as bolts used for fastening and connecting major components [2]. These components, found in the acquired power line images, exhibit characteristics such as small proportion, low resolution, easy aggregation, and indistinct features, categorizing them as small target objects. Additionally, power transmission lines are usually installed in complex natural environments, resulting in complex backgrounds in UVA-captured power transmission line images, which greatly interferes with the detection of small targets on the power lines. Therefore, improving the resolution of small transmission line targets in the images, enhancing their feature extraction, filtering complex backgrounds, and achieving high-precision identification and positioning for real-time and accurate detection pose challenging problems. The object detection algorithm based on deep learning has shown great potential in enhancing object resolution, improving feature extraction, and achieving high-precision positioning in images. As a result, it has gained wide application in the domain of small-target detection in transmission lines. This algorithm can be broadly categorized into two main types:  
1. Two-stage detection algorithms, which are mainly represented by R-CNN [3], Fast -RCNN [4], Faster R-CNN [5], etc. Such algorithms usually generate target candidate regions and then extract features through convolution neural networks to achieve classification and localization predictions for target objects. In the literature [6], based on Faster R-CNN, a feature pyramid network (FPN) was proposed as a way of replacing the original RPN and fusing high-level features, rich in semantic information, with lower-level features, rich in positional information, in order to generate feature maps of different scales. Then, the target of the corresponding scale is predicted based on the feature map of different scales. 2. Single-stage detection algorithms, which are mainly represented by SSD [7], YOLO [8; 9; 10; 11] series, etc. Such algorithms can achieve target classification and positioning prediction by directly extracting features through convolution neural networks. [PERSON] et al. proposed a YOLO [8] detection algorithm that divides the image into s x s grids and directly predicts category probability and regression position information based on the surrounding box, which corresponds to each grid. This method does not generate candidate regions and improves the prediction speed. In the same year, [PERSON] et al. [7] put forward the SSD algorithm, which draws on the idea of the YOLO algorithm and uses multi-scale learning to detect smaller targets on shallow-feature maps and larger targets on deeper-feature maps. Then, Ultralytics, a particle physics and artificial intelligence startup, proposed a single-stage object detection algorithm, YOLOv5 [11]. It uses a deep residual network to extract target features and combines the feature pyramid network FPN and perceptual adversarial network (PAN) [12] to efficiently fuse rich low-level and high-level feature information.