Dark-Center Based Insulator Detection Method in Foggy Environment

Abstract

In foggy environments, outdoor insulator detection is always with low visibility and unclear targets. Meanwhile, the scale of haze simulation insulator datasets is insufficient. Aiming to solve these problems, this paper proposes a novel Dark-Center algorithm, which is a joint learning framework based on image defogging and target detection. Firstly, the dark channel prior algorithm is used to calculate the foggy sky image transmittance and then transpose it to the original image to generate a foggy-simulated insulator dataset; secondly, the defogging and restoration modules and an optimized defogging module are combined to improve the robustness of the defogging algorithm; then, for small insulator detection, the CenterNet network structure is improved to enhance the feature extraction capability for small targets; finally, the target detection accuracy in foggy environments is improved by jointly learning the structure details and color features recovered in image defogging via the defogging model and the target detection model, which effectively learn the structure details and color features recovered in image defogging. The experimental results on the CPILD dataset show that the proposed Dark-Center algorithm based on image defogging and target detection can effectively improve the performance of the target detector in foggy scenes, with a detection accuracy of 96.76%.