# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 1 Introduction
It realizes multi-scale learning and effectively improves the detection performance of small targets. In conclusion, the two-stage detection algorithm is capable of identifying and categorizing small targets using deep features. However, it fails to adequately capture the rich detailed characteristics of small targets present in shallow features. Moreover, deep features are obtained through multiple down-sampling, which often results in the loss of the fine details and spatial features of the small target, making it challenging to accurately determine their locations. On the other hand, the single-stage detection algorithm employs multi-scale feature fusion, which addresses the multi-scale issue. Nevertheless, in multi-scale feature fusion, the fusion of shallow features and deep features is not sufficient, which leads to the unsatisfactory detection accuracy of small targets. Additionally, small targets are often subject to complex backgrounds and susceptible to noise interference, limiting the algorithm's ability to eliminate such complex background interference. Taking into account the shortcomings and deficiencies of the existing methods, an improved method called P2E-YOLOv5 (P2-ECA-EIOU-YOLOv5) is proposed in this paper. This method not only fulfills the requirements of real-time detection but also achieves the highly precise detection of small targets in transmission lines. The specific implementation of this method is as follows:  
1. To address the challenge of complex backgrounds and susceptibility to environmental interference in small targets, we enhance the network of capability to focus on these targets and mitigate environmental disturbances by incorporating the ECA (Efficient Channel Attention) [13] module. This addition improves the network's attention toward small targets, while simultaneously reducing the impact of environmental interference;
2. To cater to the imaging characteristics of small size and lower-solution targets, a high-resolution P2 detection head is integrated into the network to enhance the detection ability for small targets;3. Considering that small target features are not always obvious and tend to aggregate, we use the EIOU_Loss [14] as the regression loss function of the network to improve the accurate identification and positioning of small targets. In order to compare the characteristics of existing target detection algorithms and the proposed method more concisely, we have drawn a detailed research motivation diagram, as shown in Figure 1. Among them, the existing method uses Faster-RCNN [5] as the representative network framework, and the method adopted in this paper uses the improved YOLOv5 (P2E-YOLOv5) as the network framework. By randomly selecting four small target images of transmission lines for testing, the expected detection results are shown in Figure 1.  
Furthermore, the proposed P2E-YOLOv5 detection algorithm in this study significantly enhances the resolution of small target feature maps of transmission lines. It effectively extracts relevant features of small targets and reduces the influence of environmental factors, making it highly suitable for small-target detection in transmission lines. Moreover, this algorithm demonstrates excellent detection performance when applied to other domains, such as facial defect detection and aerial images.