# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 1 Introduction
[15] proposed a combination method of Faster RCNN and U-Net, while Faster RCNN was used to locate glass insulator strings, and U-Net was used to accurately classify pixels in cropped images of different sizes, the detection precision and recall rate are 94.9% and 95.4%, respectively. [PERSON] et al. [16] designed a box-point detector for transmission line insulator defect detection, which was composed of DCNN and two parallel convolution heads. The defect detection of insulators was divided into the aggregation task of box location and point estimation, and the test accuracy reached 94%. [PERSON] et al. [17] used YOLOv2 to detect insulators in UAV aerial images, and extracted the target area of insulators. Then, different classifiers, including SVM, CNN, Bayes net, etc., were used to identify the insulator surface conditions, which can accurately identify four surface conditions like clean, water, snow, and ice. [PERSON] et al. [18] constructed the Mask RCNN model and designed appropriate network parameters, so as to achieve detection of insulator targets in the infrared image. Then, the temperature distribution of the insulator was extracted by a proper fitting function to evaluate the condition of insulators. [PERSON] et al. [19] proposed a novel two-step method for insulator faults detection, which considers CNN features, and unique color and area features. Firstly, the region of interest (RoI) of insulator was extracted, and then the defect region was marked through Grab-cut and adaptive morphology algorithm. [PERSON] et al. [20] used the feature pyramid network (FPN), generalized intersection over union (GIoU) and region of interest align (RoI-Align) to build an exact region-based CNN (Exact R-CNN), and used U-Net to generate the mask of insulators and eliminate the complex background. The cascaded network of U-Net and Exact R-CNN can effectively detect insulator defects. [PERSON] et al. [21] applied the MnasNet network as feature extraction network to improve the SSD detection algorithm, and two multiscale feature fusion methods were used to fuse multiple feature maps, which can achieve 93.4% detection accuracy on the insulator and spacer. However, most of them focus on the improvement of detection accuracy, while the detection speed of inspection images lacks necessary considerations, which is difficult to meet the requirement of real-time detection. In practical engineering applications, the object detection algorithms can achieve real-time detection on high performance computers such as servers. However, a lot of time has been wasted in the process of image acquisition, transmission, encryption, and detection, which indirectly makes it difficult for inspectors to take measures in a timely manner. Moreover, it is difficult to obtain the detection results due to insufficient processor performance when the model was transplanted to the embedded devices such as mobile phones. Therefore, in order to improve the speed of the object detection model and facilitate the deployment of the model in practical engineering applications, many researchers have made lightweight improvements to the existing object detection algorithms. For example, [PERSON] et al. [22] improved the CSPDarknet53 of YOLOv4 to the CSP1_X module, and used the CSP2_X module to enhance the feature extraction, while the adaptive image scaling was used to process the input image. The improved model can effectively reduce the computation and redundancy, which can reach the mAP of 98.3% and the 54.57 frame per second (FPS) in face mask recognition. [PERSON] et al. [23] used DenseNet and two new residual blocks to optimize the backbone and neck, which improves the feature extraction and reduces the computing cost. The test results can reach a precision of 90.33% and a detection speed of 70.19 FPS in detecting four different diseases in tomato plants. [PERSON] et al. [24] used MobileNetv2 to replace the feature extraction network of YOLOv4 to realize the lightweight of the detection network. [PERSON] et al.