The thickness of the coating layer of coated fuel particles plays a vital role in the reliable operation of a high-temperature gas-cooled reactor. Coating thickness remains extremely challenging to measure it efficiently, accurately, and robotically. The existing methods struggle with poorly refined segmentation results and easy adhesion of boundaries, complicating the accurate image segmentation of coating layers, which is fundamental to achieving the measurement goal. To overcome these issues, we designed the Context-Ensembled Refinement Network. This network refines the segmentation results to accurately extract different coating regions, leveraging its context fusion module and boundary refinement module to effectively utilize multiscale contextual information and provide strong boundary refinement capability. Experimental results demonstrate that our proposed method outperforms recent state-of-the-art methods, achieving an mIoU value of 95.8 %, and holds promising potential for automatic measurement of coating thickness.