Insulator and Defect Detection Model Based on Improved Yolo-S

Abstract

A large number of insulators play an important role in insulating and supporting complex power grids, and they are constantly exposed to challenges such as lightning strikes and contamination from the external environment. Only by accurately detecting insulator damage can potential safety hazards and equipment damage due to damaged insulators be avoided in time. Aiming at the problems of low accuracy and large model computation of existing insulator and defect detection algorithms, this paper proposes the insulator and defect detection model YOLO-S (YOLO-Small). First, the PAN structure in Neck uses lightweight convolutional GSConv instead of the standard convolutional Conv, introduces GSbottleneck on the basis of GSConv, and uses the method of OSA to design the cross-level partial network GSCSP module (VoV-GSCSP). The use of VoV-GSCSP instead of the C3 module in Neck reduces the computational effort and maintains the accuracy. Secondly, a new attention module, MaECA (MainECA), is designed based on the ECA attention mechanism to enhance target perception. After that, the SILU function in the SPPF in YOLOv5s is replaced with the Mish function, HardSwish function, and ReLU function, respectively, and the results show that the replacement of the Mish function (MishSPPF) is more effective in preventing the distortion of the image caused by image cropping and scaling, and thus improving the accuracy. Finally, the SIoU loss function is used to replace the original loss function CIoU, which improves the number of transmitted frames per second and the detection accuracy of the pictures. The mAP of YOLO-S is 4.2% higher than that of YOLOv5s, the detection accuracy is improved by 2.1%, and the model parameter computation is reduced by 6.0%, which still gives YOLO-S a higher recognition accuracy when compared with existing detection algorithms under the same conditions.