Rapid expansion of urban areas and population growth is causing an immense increase in waste production, which demands the need for efficient and automated waste management. In this scenario, automated waste recycling (AWR) using deep learning methods can assist humans in optimal waste management. Recent deep learning approaches for AWR provide promising waste segmentation performance, however, these methods rely on large backbone networks that are inefficient for AWR systems and suffer from performance deterioration in cluttered scenes. To this end, an optimal waste segmentation network is introduced which effectively utilizes the spatial domain to capture localized structural dependencies and the spectral domain to efficiently extract global contextual relationships. This cascaded design allows the network to progressively leverage both local and global representations across complementary domains to highlight the semantic information necessary for effective segmentation of various waste objects. Furthermore, auxiliary feature enhancement module (AFEM) is introduced to enhance the target objects' boundaries and blob amplification for better segmentation in cluttered scenarios. Extensive experimentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets reveals the merits of the proposed method.

翻译：城市区域的快速扩张和人口增长导致废弃物产量急剧增加，这亟需高效自动化的废弃物管理方案。在此背景下，基于深度学习的自动化废弃物回收（AWR）技术可辅助人类实现最优废弃物管理。现有AWR深度学习方法虽展现出有前景的废弃物分割性能，但这些方法依赖大型骨干网络，不仅对AWR系统而言效率低下，且在杂乱场景中会出现性能退化问题。为此，本文提出一种最优废弃物分割网络，该网络有效利用空间域捕获局部结构依赖性，并通过频谱域高效提取全局上下文关系。这种级联设计使网络能够渐进式地在互补域中协同利用局部与全局表征，突出有效分割各类废弃物对象所需的语义信息。此外，本文引入辅助特征增强模块（AFEM），通过强化目标对象边界与斑点放大机制提升杂乱场景下的分割效果。在ZeroWaste-aug、ZeroWaste-f和SpectralWaste数据集上的大量实验验证了所提方法的优越性。