Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance

Panoptic segmentation, a pivotal research direction in computer vision, unifies pixel-level object and background recognition within a scene, crucial for applications like autonomous driving. However, existing methods, including State-of-the-Art models like Mask2Former, often exhibit limitations such as inadequate adaptation in multi-scale feature fusion and ambiguous boundary segmentation, particularly for small objects in complex scenes. To address these specific challenges, we propose a novel network: PSM-FFEG (Panoptic Segmentation Model with Feature Fusion and Edge Guidance). PSM-FFEG introduces three key components: (1) A dynamic multi-scale feature fusion module enhancing contextual modeling via cascaded convolutions and adaptive attention. (2) An explicit edge guidance module refining boundary features with dedicated supervision. (3) A dual-path Transformer decoder optimizing cross-path feature interaction between pixels and queries. Extensive experiments on the Cityscapes and MS COCO datasets demonstrate that, using a ResNet50 backbone, PSM-FFEG achieves 2.6% and 2.4% absolute improvements in panoptic quality (PQ) over the Mask2Former baseline, respectively. Notably, PSM-FFEG shows significant gains for small objects on Cityscapes, with PQ increasing by 4.3% for traffic lights and 6.8% for motorcycles. These results validate the effectiveness of the proposed modules. To foster further research, our implementation code will be made publicly available.