Abel-Net: Aggregate Bilateral Edge Localization Network for Multi-Task Binary Segmentation

Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention (EA) and Edge Fusion Attention (EFA) modules prioritize edge-critical regions and facilitate accurate boundary alignment. Extensive experiments on nine diverse binary segmentation tasks demonstrate that Abel-Net performs comparably to or surpasses state-of-the-art task-specific networks, exhibiting strong adaptability to a wide range of visual perception challenges.