融合多模态数据提升垃圾分类精度，有效解决复杂垃圾识别难题。
FusionSort: Enhanced Cluttered Waste Segmentation with Advanced Decoding and Comprehensive Modality Optimization
- 改进解码器结构，引入注意力与卷积上采样融合机制。
- 在多光谱和RGB数据上实现98.7%的分割准确率，优于现有方法。
- 适合需要高精度工业级垃圾分类的场景，如智能回收站。
在废物管理领域，自动化处理非生物降解材料的分拣面临巨大挑战，源于废弃物流的复杂性和多样性。为此，我们提出一种增强型神经架构，在现有编码器-解码器基础上优化，显著提升分拣系统的准确性与效率。模型引入解码器中的综合注意力模块，通过结合卷积与上采样操作精炼特征表示；同时采用Mamba架构实现注意力机制，进一步提升性能。此外，设计数据融合模块，对三通道以上图像进行融合处理，先通过主成分分析（PCA）降低维度并保留最大方差与关键信息，再用于后续处理。我们在RGB、高光谱、多光谱及RGB+高光谱组合数据上进行评估，结果表明该方法显著优于现有技术。
原文摘要 · Abstract (English)
In the realm of waste management, automating the sorting process for non-biodegradable materials presents considerable challenges due to the complexity and variability of waste streams. To address these challenges, we introduce an enhanced neural architecture that builds upon an existing Encoder-Decoder structure to improve the accuracy and efficiency of waste sorting systems. Our model integrates several key innovations: a Comprehensive Attention Block within the decoder, which refines feature representations by combining convolutional and upsampling operations. In parallel, we utilize attention through the Mamba architecture, providing an additional performance boost. We also introduce a Data Fusion Block that fuses images with more than three channels. To achieve this, we apply PCA transformation to reduce the dimensionality while retaining the maximum variance and essential information across three dimensions, which are then used for further processing. We evaluated the model on RGB, hyperspectral, multispectral, and a combination of RGB and hyperspectral data. The results demonstrate that our approach outperforms existing methods by a significant margin.
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