To guarantee the safety of the power grid system, it is essential to proceed reliable powerline inspection. Insulators are
key devices in the powerlines. Their major function is to achieve mechanical fixing and electrical insulation, they play a key
role in power lines. Insulators are deployed outdoors. Therefore, ensuring the safe operation of insulators is significant in the
powerline inspection. Among all the inspection method, visual inspection is the key way. However, problems such as large
changes in outdoor lighting have a strong impact on the accuracy of insulator detection. To overcome the shortcomings of
uneven illumination, low contrast and poor details display in outdoor images, in this paper we introduces an image enhancement
method based on illumination correction and compensation. First, the input data is converted from RGB color space to the HSV
space, and three components, H, S and V, are obtained. The saturation component S is enhanced adaptively, and the brightness
component V is processed by multi-scale gradient domain guided filter (MGDGF). Then the illumination component of the
image is extracted, and corrected by two-dimensional adaptive Gamma transformation. The new brightness component is fused
by Retinex based models. It helps to enhance the dark details and overall brightness of the image. This method not only
solves the uneven illumination problem of the image, but also improves the contrast and details, while maintaining the original
naturalness. Further, we introduce a real-time one step detection model based on YOLOv5, to detect the defect of the insulator.
We evaluate the proposed method on an open public dataset. The evaluation results demonstrate that our proposed method can
get very competitive results while maintaining real-time performance.