Yue Xu ^1,2,3 Runze Liu ^1,2 Dongchen Zhu ^1,3* Lili Chen ^1,2 Xiaolin Zhang ^1,2,3* Jiamao Li ^1,3*

• ¹ Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences (CAS), Changning, China
• ² School of Information Science and Technology, ShanghaiTech University, Shanghai, Shanghai Municipality, China
• ³ University of Chinese Academy of Sciences, Beijing, Beijing, China

Panoptic segmentation plays a crucial role in enabling robots to comprehend their surroundings, providing fine-grained scene understanding information for robots' intelligent tasks. Although existing methods have made some progress, they are prone to fail in areas with weak textures, small objects, etc. Inspired by biological vision research, we propose a cascaded contour-enhanced panoptic segmentation network called CCPSNet, attempting to enhance the discriminability of instances through structural knowledge. To acquire the scene structure, a cascade contour detection stream is designed, which extracts comprehensive scene contours using channel regulation structural perception module and coarse-to-fine cascade strategy. Furthermore, the contour-guided multi-scale feature enhancement stream is developed to boost the discrimination ability for small objects and weak textures. The stream integrates contour information and multi-scale context features through structural-aware feature modulation module and inverse aggregation technique. Experimental results show that our method improves accuracy on the Cityscapes and COCO datasets while also demonstrating robustness in challenging simulated real-world complex scenarios faced by robots, such as dirty cameras and rainy conditions.