# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 2 Related Work  
Transmission line small-target detection is a challenging problem in the field of target detection, and it has gradually become a research hotspot both domestically and internationally in recent years. [PERSON] et al. [15] proposed a method for detecting small-target defects of transmission lines based on the Cascade R-CNN algorithm. Specifically, they used the ResNet101 (Residual Network) [16] network to extract target features and employed a multi-hierarchy detector to identify and classify small targets of transmission lines. Experimental results  
Figure 1: In the Figure, the presence of blue circles denotes the presence of diminutive undetected targets that eluded the grasp of the detection algorithm. Upon juxtaposing the anticipated outcomes of these two methodologies, it becomes conspicuously apparent that the suggested approach exhibits superior performance. demonstrate that this algorithm significantly improves the detection accuracy of small-target defects in transmission lines. [PERSON] et al. (2017) and others proposed an improved SSD small-target detection algorithm, named PA-SSD. They integrated deconvolution fusion units into the PAN algorithm to fuse feature maps of different scales, enhancing multi-scale feature fusion. Additionally, they established a new feature pyramid model by replacing feature maps in the original SSD algorithm. Experimental results show that the improved algorithm can extract feature maps with higher resolution, enhance feature extraction for small targets, and significantly improve the detection accuracy of power components in transmission lines while maintaining satisfactory detection speed. [PERSON] et al. (2018) proposed an effective approach to expand the foreign body data set of transmission lines using scene enhancement, mix-up, and noise simulation. This method reduces the problem of limited pictures in the data set and enhances the diversity of the data set, leading to improved detection performance of the network on small targets. [PERSON] et al. (2019) proposed a perceptive GAN method specifically designed for small-target detection. Their approach uses generators and discriminators to learn high-resolution feature representations of small targets from each other. [PERSON] et al. (2020) enhanced the feature extraction capability of the SSD network by replacing the original VGG network with the more powerful ResNet network. Furthermore, they integrated FPN into the network structure to achieve information fusion between upper and lower feature layers. The experimental results demonstrate significantly improved accuracy in detecting small and medium-scale targets in transmission line images compared to the original SSD algorithm. [PERSON] et al. (2021) proposed an object detection algorithm based on the improved CenterNet. They built a multi-channel feature enhancement structure and introduced underlying details to improve the low detection accuracy caused by using a single feature in CenterNet. The research results show that the proposed algorithm achieves good detection results for power components and abnormal targets of transmission lines. Additionally, [PERSON] et al. (2021) proposed an insulator detection and defect identification algorithm based on YOLOv4. The enhanced algorithm utilizes feature multiplexing to improve the residual edge of the residual structure, significantly reducing the number of parameters and computational complexity of the model. The SA-Net (Random Attention Neural Network) ([PERSON] et al., 2018) attention model is integrated into the feature fusion network to strengthen the focus on the target features and enhance their significance. Moreover, multiple outputs are added to the output layer to improve the model's capability to identify small, damaged insulator targets. Experimental results demonstrate that the enhanced algorithm significantly reduces the number of parameters while greatly improving the accuracy of insulators and defects detection. Recently, [PERSON] et al.