# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8  
[PERSON]  
1 Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China; [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (W.W.); 1  
[PERSON]  
1 Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China; [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (W.W.); 1  
[PERSON]  
4 Hunan Province Key Laboratory of Intelligent Live Working Technology and Equipment (ROBOT), State Grid Hunan Ultra-High Voltage Transmission Company, Changsha 420100, China; 2 [EMAIL_ADDRESS] 4  
[PERSON]  
1 Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China; [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (W.W.); 1  
[PERSON]  
1 Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China; [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (W.W.); 1  
Footnote 1: email: chuang.cong@cong.cong. two-stage algorithms include R-CNN [4], Fast R-CNN [5], and Faster R-CNN [6; 7], while classic single-stage algorithms include the YOLO [8; 9; 10; 11; 12] and SSD [13]. Among these, the YOLO series of algorithms, known for their rapid detection speed and broad applicability, have been extensively improved and utilized by many researchers for various detection tasks. For example, [PERSON] et al. [14] proposed the BL-YOLO algorithm, which combines attention mechanisms and the BiFPN structure to detect road defects, achieving an average recognition accuracy of 90.7%. [PERSON] et al. [15] successfully employed YOLOv8 to differentiate between drone control signals and other signals, attaining an average recognition accuracy of 90%. The YOLO series of algorithms has also demonstrated significant potential in the field of insulator fault diagnosis. For instance, [PERSON] [16] recently introduced a Hybrid-YOLO algorithm that combines ResNet-18 with YOLO, achieving high-precision insulator fault diagnosis. This approach outperforms architectures such as VGG16, ResNet-50, and DenseNet-152, highlighting the strong performance of YOLO algorithms in the domain of insulator faults. [PERSON] et al. [17] improved YOLOv5 by incorporating the Mish activation function and the CBAM attention mechanism, enabling effective insulator fault diagnosis under varying weather conditions and significantly enhancing diagnostic accuracy. [PERSON] et al. [18] designed an attention mechanism that integrates channel and spatial information to improve the YOLOv5 algorithm. This enhancement enables the model to rapidly locate the area of the insulator and facilitates quick diagnosis of insulator faults. Although the aforementioned studies have contributed to insulator fault diagnosis, most of them are limited in their ability to diagnose a single type of insulator fault and still have room for improvement in detection accuracy. Additionally, the presence of diverse backgrounds and varying fault scales in insulator images poses challenges for existing models, which struggle to effectively extract and fuse multi-scale target feature information. Lastly, the challenge of diagnosing small faults with varying scales remains significant for these models. With the continuous iterations of YOLO series algorithms, YOLOv8 has demonstrated remarkable potential. For instance, [PERSON] [19] compared the detection performance of YOLOv1 through YOLOv8, revealing the robust capabilities of YOLOv8 in the field of object detection. [PERSON] et al. [20] integrated YOLOv8 with a drone-based image acquisition system, enabling rapid detection of insulator faults, which highlights YOLOv8's superior detection speed and accuracy. This paper selects the YOLOv8 algorithm as the baseline model for optimization to achieve high-precision diagnosis of insulator faults such as damage, self-destruction, and flashover.