The field of image segmentation and inspection is witnessing significant advancements, driven by the need for efficient and accurate analysis of complex data. Researchers are exploring innovative techniques to improve the robustness and generalization of segmentation models, particularly in adverse weather conditions and complex scenarios. The integration of mode normalization, geometric abnormality suppression, and reflectance distortion calibration are being investigated to enhance the performance of existing models. Additionally, the development of novel frameworks and architectures, such as those leveraging Bidirectional Encoder Representations and structural priors, are demonstrating strong performance in automotive damage evaluation and car damage segmentation. Noteworthy papers include U-NetMN and SegNetMN, which accelerate convergence and improve stability in SAR image segmentation, and SLICK, which introduces a selective localization and instance calibration framework for knowledge-enhanced car damage segmentation. These advancements have the potential to significantly impact various applications, including remote sensing, automotive inspection, and insurance workflows.