###### Abstract
Finally, a YOLO-v3 tiny model was used to detect the missing faults of insulators in RoI. The experimental results demonstrate that the average running time of the proposed model could reach 30 ms per image, which had the potential for real-time fault detection from high-voltage transmission lines. In order to promote feature reuse and propagation, in the work of [38], dense blocks were adopted to YOLO-v3 for traffic sign detection in real scenes, and the average precision of the modified YOLO model could reach 95.92%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Real-Time** & **Limitation** & **Applicable Scenes** \\ \hline R-CNN & No & Detection is time-consuming and does not share features & Object detection \\ Fast R-CNN & No & Extracting object candidate boxes is time-consuming & Object detection \\ Faster R-CNN & Poor & Model is complex and the small target detection effect is not good & Object detection \\ R-FCN & Poor & Model is complex and large amount of calculation & Semantic segmentation \\ Mask R-CNN & Poor & The cost of instance segmentation is expensive & Instance segmentation \\ YOLO-v1 & Excellent & The locating error is large and the detection accuracy is low & Object detection \\ SSD & Excellent & Features of small object is lost & Multi-scale detection \\ YOLO-v2 & Excellent & The effect of small object detection is poor & Object detection \\ YOLO-v3 & Excellent & Recall of the model is low & Multi-scale detection \\ YOLO-v4 & Excellent & The detection accuracy needs to be improved & High precision detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparative analysis of some representative object detection networks. In a word, the application of deep learning methods for insulator faults detection has very considerable prospects. However, there are several challenges for insulator fault detection in the inspection of transmission lines, including the lack of insulator fault images, aerial images with complex background interference, insulator faults being small compared with the size of the insulator, and so on. To solve the above issues, inspired by the work of [38] and YOLO models, this paper proposed a Cross Stage Partial Dense YOLOv3 (CSPD-YOLO) model for insulator fault detection in aerial images with complex backgrounds. A novel insulator faults dataset was created to solve the lack of insulator fault images. To improve feature reuse and propagation in the low-resolution feature layers, Cross Stage Partial Dense (CSPD) blocks were adopted to the feature extraction network of the CSPD-YOLO model. To obtain multi-scale features of insulator faults, a structure of feature fusion was adopted into CSPD-YOLO model. To improve the detection accuracy of insulator faults, an improved loss function and the k-mean++ clustering algorithm were adopted to the CSPD-YOLO model. The rest of this paper is organized as follows: (1) Section 1 reports the existing works of insulators and their fault detection; (2) Section 2 details the modified network CSPD-YOLO model; (3) Section 3 gives experimental results and analysis; (4) and finally, Section 4 presents the conclusion of this paper.