Insulator is one of the important components of overhead lines. When there is a fault, it will affect the safe operation of power grid. In order to realize rapid and accurate identification of insulator fault, an insulator fault detection method based on improved YOLOv3-Tiny is proposed. Firstly, in order to enhance the small target detection ability, the shallow feature map and the feature map before the second detection layer are spliced in the same dimension to construct the third prediction layer. Then, the network uses Ghost module to replace the convolution layer in the backbone network and reduce the parameters of the model. Then, a new attention module MECA (multiscale efficient channel attention) is designed to enable the network to focus on the salient characteristics of insulators. Finally, a new effective intersection over union (EIoU) is proposed as the frame regression loss function to better locate the insulator position. The experimental results show that the average accuracy (MAP) of the improved YOLOv3-Tiny algorithm in insulator fault detection is as high as 96.1%, which is 17% higher than that of the original YOLOv3-Tiny.