## 1 Introduction  
Intelligent video surveillance systems are frequently utilized in electric power operation settings as a result of the advancement of computer vision and other artificial intelligence technologies. Affected by weather, light, and other influences, there are problems such as insufficient light, uneven light, and backlight in the electric power operation scene, which leads to poor image visibility, and the low-light image seriously affects the accuracy of target detection and operator-behavior recognition tasks in the operation scene. Figure 1 shows a comparison of low-light and normal images and their grayscale histograms at an electric power operation site. A large number of misdetections and omissions occur in low-light images (a) and (b) for the detection of skeletal keypoints of electric power operators, which seriously affects the use of skeletal keypoints in the behavioral monitoring of operators. Comparing the grayscale histograms, the grayscale histograms of the low-light images show uneven pixel distribution, with pixels of a smaller grayscale accounting for most of the pixels, while the grayscale distribution of the normal image (c) is more balanced. Therefore, studying the low-light image enhancement method for electric power operation sites is crucial for item detection and personnel behavior recognition at the operation site.  
Due to the special characteristics of the electric power industry, strong light sources are often used to augment light operations at night; for example, in Figure 1 image (d), although the strong light source enhances the local brightness, it interferes with the low-light enhancement, resulting in local overexposure. So, the low-light image enhancement of the electric power operation site needs to be considered as a suppression of the strong light effect.  
For electric power operation sites, we suggest a low-light image enhancement method that takes strong light suppression into account, which is different from other current low-light enhancement methods from the perspective of practical applications in electric power operation, and considers both the overall low-light image and the presence of a strong light source in low light. Our contributions are summarized as follows:  
* We designed a strong light judgment method based on sliding windows, which used a sliding window to segment the image; a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold in order to search for strong light.
* We used a light effect decomposition method based on a layer decomposition network to decompose the light effects of RGB images in the presence of strong light to eliminate the light effect layer and to reduce the interference of strong light effects on the enhancement of low-light images.
* We constructed a Zero-DCE low-light enhancement network based on a kernel selection module, with a significant decrease in the number of parameters (Params) and the number of floating-point operations (FLOPs) compared with the original Zero-DCE; the subjective visual quality and objective evaluation metrics of the enhanced images outperformed those of other current state-of-the-art methods.