###### Abstract  
Insulator fault detection is one of the essential tasks for high-voltage transmission lines' intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background. fault detection; aerial image; complex background; deep learning; image processing; intelligent inspection +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model. _Appl. Sci._**2021**, _11_, 4647. Many researchers used aerial images and traditional image processing technology to identify insulators and their faults [7, 8, 9, 10]. Generally, traditional image processing algorithms segmented insulators through specific features (e.g., color, gradient, texture, shape, etc. ), and then implemented fault detection through matching algorithms. However, these algorithms relied on manually designed classification features, and these manually designed features are not suitable for multi-scale detection in complex backgrounds. As the correlation features of insulators in aerial images are uncertain, the accuracy of traditional image processing algorithms is reduced. In recent years, with the rapid development of deep learning theory, image processing methods based on deep neural networks have been widely used in the field of object detection and classification, which have several advantages over traditional image processing methods [11, 12, 13, 14, 15]. Object detection methods based on deep neural networks are composed of convolutional neural networks (CNN), which are capable of extracting image features automatically and learning from different environmental conditions. With the widespread application of CNN, deep learning methods overcome the limitations of the traditional image processing techniques, thus exhibiting high performance and greatly improving the accuracy of object detection [16, 17, 18]. In the task of object detection, the existing deep learning methods can be divided into two categories--one-stage networks and two-stage networks [19, 20]. Two-stage networks are regional suggestion methods which have a region regarding the proposal of interest and a separate region regarding the classification of the object, firstly generating candidate regions of interest, and then carrying on with the classification of extracted features. Typical two-stage networks include regions with convolutional neural networks (R-CNN) [21], fast R-CNN [22], faster R-CNN [23], region-based fully convolutional networks (R-FCN) [24], Mask R-CNN [25], and so on. In some public datasets, the detection accuracy of two-stage networks is slightly higher than that of one-stage networks; however, the detection speed of two-stage networks is far inferior to that of one-stage networks.