# Dark-Center Based Insulator Detection Method in Foggy Environment
###### Abstract
Among the above algorithms, the dark channel prior theory [18] based on the atmospheric scattering model is more effective, has higher detection accuracy, and fast running speed. Thus, it is adopted in this paper to achieve the effect of defogging by incorporating the method into the target detection network. Target detection algorithms can be divided into two categories according to the presence or absence of a preset anchor frame as a benchmark: anchor-free methods and anchor-based methods. Anchor-free algorithms mainly include ConerNet [19], CenterNet [20], FCOS [21], etc., and anchor-based mainly include YoLo series [22], SSD [23], RetinaNet [24], etc. Since anchor-based methods require setting different anchor frames in different datasets, and the setting of anchor frames will have an impact on the accuracy, non-extreme value suppression is also required, resulting in slow running speed [25]. To address the above drawbacks, anchor-free target detection algorithms have been gradually developed. For CenterNet networks, anchor-free algorithms treat a target as a point, and one target is determined using one feature point. Its prediction result will divide the input image into different regions, and each region will have one feature point. Then, it will be determined whether an object exists or not in each feature point, as well as its type and confidence, according to the prediction result. The algorithms will also adjust the feature point to obtain the center coordinates and the width and height of the object. Currently, target detection methods in foggy environments can be mainly divided into two categories: a two-stage method and an end-to-end approach. The two-stage method is an uncorrelated target detection method based on defogging detection. It defogs the fogged image using image enhancement and recovery methods and then detects the defogged image using target detection methods. The defogging process may bring problems such as artifacts, color distortion, and fog residue to the image, so not all images detected using defogging methods will improve their detection accuracy. The end-to-end approach, on the other hand, jointly optimizes the training of the defogging network and the target detection network to perform the defogging task and the detection task simultaneously [26; 27; 28] to solve the above problems. Therefore, this paper adopts an end-to-end approach to detect targets in foggy scenes to improve detection accuracy. As we all know, it is essential to construct the high-resolution foggy insulator image datasets taken by UAVs, then to locate and detect the \"self-exposure\" areas of insulators with the help of image-processing and deep-learning technologies to realize intelligent power inspection. As stated above, to address the challenges of foggy environment target detection, this paper proposes a joint learning framework Dark-Center foggy insulator detection and identification algorithm based on CenterNet, which is called Dark-Center detection for short. Its characteristics are outlined as follows:  
1. Due to the lack of large-scale foggy insulator datasets, this paper builds foggy image dataset CPILDs using the haze simulation method to construct insulator haze images;
2. To address the problem of poor robustness of most current defogging algorithms, this paper proposes a novel image-defogging algorithm with an optimized defogging and repairing process;
3. The Center algorithm is proposed to enhance the detection capability of small insulators to solve the problem of insufficient feature extraction capability of the detection network;
4. This paper jointly optimizes the training of the defogging algorithm and the target detection algorithm to overcome the problems of artifacts, color distortion, and fog residue.