This paper proposes DTL-IceNet, a dual-task learning–based network for detecting icing on overhead transmission lines, designed to accurately identify both the type and the thickness of ice. The study demonstrates that, by segmenting the background and isolating the ice-covered regions, the method achieves high accuracy in distinguishing different icing types. Furthermore, based on the segmentation and classification outputs, the authors estimate ice thickness by incorporating meteorological data, and the reported results indicate strong performance, highlighting a promising direction for further research. In addition, the authors’ decision to release the dataset and source code is particularly commendable and will significantly benefit the research community.

Major comments

It is recommended to supplement the Summarize section or add a dedicated Discussion section. This section should include a more in-depth analysis of the reasons why the model performs well or fails under certain conditions, a discussion of the model’s limitations (for example, its performance under significant terrain variations or extreme weather conditions not represented in the dataset), and a more balanced interpretation of the results in the context of existing literature.

The manuscript presents two distinct results: (1) MOMSA-SegNet achieves the highest segmentation mIoU, and (2) the overall framework reports a thickness MAPE of 11.82%. To clarify the relationship between these two findings, additional controlled experiments would be helpful. In particular, demonstrating that, under the same test set and using the same thickness estimation procedure, the proposed segmentation model yields a consistently lower thickness estimation error compared with the other segmentation models discussed in the paper would provide more direct evidence of its contribution. Without such comparisons, the extent to which the segmentation module influences the final estimation accuracy remains uncertain.

Regarding environmental conditions, although the segmentation component includes descriptions of performance under different weather scenarios, the influence of these varying conditions on the final thickness estimation results is not examined. Expanding the Discussion section to include an evaluation of thickness estimation performance across different weather conditions would help provide a more complete understanding of the method’s behavior.

The manuscript claims to propose a comprehensive “dual-view” solution. While the approach of collecting real thickness data in controlled field experiments is understandable and commendable, the current experimental setup does not sufficiently validate the core contribution of the method, and instead highlights certain limitations. Specifically, the final performance evaluation is conducted on a restricted, single-view version of the system. This creates a substantial mismatch between the claimed capabilities and the empirical validation.We note that the authors provide “site conditions” as a rationale for this choice. However, this results in the core claim of the method—that leveraging multi-view structures from a single image enhances information capture—remaining unverified in thickness estimation experiments. By effectively omitting the higher-error components during validation, a critical question arises: does the reported thickness accuracy truly reflect the capability of the complete main-view and side-view system, or does it primarily represent performance in a simplified main-view scenario, which conveniently avoids the error propagation associated with the less accurate side-view segmentation?

On the other hand, achieving strong final results does not, by itself, validate the correctness or effectiveness of the front-end image segmentation plus area ratio approach. It primarily demonstrates the strength of the back-end correction module. Only with supplementary ablation experiments can it be convincingly shown that meteorological data and image information are complementary and both necessary, thereby substantiating the true value of the fusion framework.

Minor comments

Line 141 and Fig9 “glaz”->”glaze”

Table 1 Consider rephrase the table title.

Fig13 Consider rephrase the figure title.

This paper proposes DTL-IceNet (Dual-Task Learning Ice Detection Network) to identify both the type and thickness of ice on overhead transmission lines. My comments are listed below:

1. Please provide detailed information for different branch in ResSepNet in Section 3.3.1. Has results in Figure 9 been statistically tested? Please include more information on this.
2. The paper currently lacks a dedicated "Discussion" section, which limits the depth and completeness of the research. It is recommended that the authors add this section, focusing on the following three key aspects: Model Advantages, Mechanism Interpretation and Model Limitations.
3. The connection between the high mIoU of MOMSA-SegNet and the final thickness MAPE is not sufficiently demonstrated. To validate the overall framework, it is essential to provide evidence that the superior segmentation performance is a prerequisite for the low thickness error. Ablation studies comparing the impact of different segmentation qualities on the final MAPE are needed.