DTL-IceNet: A Dual-Task Learning Architecture with Multi-Scale Fusion Mechanisms for Enhanced Ice Detection on Transmission Lines

Abstract. Icing on transmission lines can significantly impact the stable operation of the power system. Deep learning-based ice image recognition is effective but remains vulnerable to background interference and noise, degrading accuracy. Moreover, when detecting ice thickness, the 2D nature of ice images introduces spatial limitations in representing the 3D ice state, which can lead to detection errors caused by a single viewpoint. To tackle the aforementioned challenges, this paper proposes DTL-IceNet (Dual-Task Learning Ice Detection Network), a transmission line icing detection network based on a dual-task learning framework, designed to accurately identify both the type and thickness of ice on overhead transmission lines. DTL-IceNet incorporates a multi-branch structured ice coating recognition module, ResSepNet (Residual & Depth-Separable Convolution Network), which segments the background and conductor areas to mitigate the influence of background noise. Additionally, a semantic segmentation module, MOMSA-SegNet (MobileOne & Multi-Scale Attention Segmentation Network) is designed to segment the ice-covered areas in both the main and side views of the image. The multi-scale attention mechanism is employed to extract spatial features from the raw icing image. When calculating ice thickness, the multi-scale fusion and correction optimization are adopted to enhance the algorithm. Experimental results show that compared with other models, the proposed method achieves an improvement of 4.17 % in icing type identification accuracy and a MAPE of 11.82 % in icing thickness detection. The application of this approach is crucial for reducing the hazards caused by ice coating on transmission lines and improving the stability of the power grid.