# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 1 Introduction  
The development of China's power industry has led to increasingly long and complex transmission lines. Insulators, as critical components, serve dual purposes: providing mechanical support and ensuring electrical insulation [1]. Exposed to the atmosphere year-round, insulators are susceptible to natural factors such as lightning strikes, pollution, and severe cold. Failures in insulators can cause transmission lines to contact towers or other lines, resulting in power outages and potentially large-scale blackouts. Consequently, regular inspections of porcelain insulators on transmission lines are crucial for the reliable delivery of electricity across the grid [2]. Traditionally, insulator inspections have been performed manually. However, inspecting high-voltage transmission lines poses significant challenges in terms of difficulty, efficiency, and safety [3]. The rapid development of UAV (unmanned aerial vehicle) technology has enabled the power system to use drones for capturing images of insulators along high-voltage transmission lines [4]. These images are then analyzed and diagnosed using image processing algorithms. Despite this advancement, the wide angle and variable perspectives of drone photography present challenges such as small target sizes and complex backgrounds, leading to low detection accuracy, weak feature map representation, and the limited extraction of key information. The object detection algorithms based on deep learning have been proven to be highly effective in identifying defects in target objects, such as the external defects of capacitors [5]. As a result, researchers are increasingly utilizing image processing and deep learning techniques to detect insulator defects. To address these challenges, this paper proposes an improved method for insulator defect detection based on the YOLOv7-tiny network. We replace the backbone network with the FasterNet architecture to reduce the model's parameters. In the neck part of the YOLO model, we introduce the OD-SlimNeck feature fusion network, incorporating full-dimension dynamic convolution in the feature extraction layer. This enhancement not only improves the performance of the lightweight network but also enhances the detection of small targets. Additionally, a decoupled detection head is used to separately predict classification and localization tasks, thereby increasing the accuracy of insulator defect detection. To further optimize the model, we applied channel pruning, significantly reducing the parameter size without a notable decrease in accuracy. Finally, to enhance detection accuracy further, we used the YOLOv7 model as a teacher model and applied channel distillation (CD) to our pruned model, thereby improving the accuracy of insulator defect detection. This research aims to employ an improved deep learning network architecture for detecting insulator defects and to lighten the model through channel pruning and channel knowledge distillation, enabling deployment on mobile devices such as drones. The academic contributions of this paper are summarized as follows:  
1. **Enhanced Feature Fusion Network**: We proposed the OD-SlimNeck network architecture for detecting insulator defects using drones. This enhancement improves the detection of small defects and prevents the loss of detailed information. By integrating multi-scale features and leveraging both local and global features, the network's feature mapping representation is enhanced, thereby improving detection performance. The full-dimension dynamic convolution allows the network to extract regions of interest from complex backgrounds and interference-laden images, focusing on key targets and further boosting the model's performance. 2. **Model Compression through Channel Pruning**: We applied the group slim channel pruning method to compress the improved model. Before pruning, the model undergoes sparsity training, ensuring that within certain pruning limits, the model's performance is not significantly compromised.