COSNet: A Novel Semantic Segmentation Network using Enhanced Boundaries in Cluttered Scenes
This addresses the challenge of automated waste recycling by improving segmentation accuracy in cluttered environments, though it appears incremental as it builds on multi-contextual methods with boundary enhancements.
The paper tackles the problem of segmenting waste objects in cluttered scenes, where existing methods degrade due to irregular shapes and translucent materials like plastic, and introduces COSNet, which achieves gains of 1.8% mIoU on ZeroWaste-f and 2.1% on SpectralWaste datasets.
Automated waste recycling aims to efficiently separate the recyclable objects from the waste by employing vision-based systems. However, the presence of varying shaped objects having different material types makes it a challenging problem, especially in cluttered environments. Existing segmentation methods perform reasonably on many semantic segmentation datasets by employing multi-contextual representations, however, their performance is degraded when utilized for waste object segmentation in cluttered scenarios. In addition, plastic objects further increase the complexity of the problem due to their translucent nature. To address these limitations, we introduce an efficacious segmentation network, named COSNet, that uses boundary cues along with multi-contextual information to accurately segment the objects in cluttered scenes. COSNet introduces novel components including feature sharpening block (FSB) and boundary enhancement module (BEM) for enhancing the features and highlighting the boundary information of irregular waste objects in cluttered environment. Extensive experiments on three challenging datasets including ZeroWaste-f, SpectralWaste, and ADE20K demonstrate the effectiveness of the proposed method. Our COSNet achieves a significant gain of 1.8% on ZeroWaste-f and 2.1% on SpectralWaste datasets respectively in terms of mIoU metric.