用像素分布学习提升垃圾图像分类效率与稳定性
Image Recognition for Garbage Classification Based on Pixel Distribution Learning
- 基于像素分布学习，替代传统CNN降低计算复杂度
- 在Kaggle垃圾数据集上实现更高分类准确率
- 适合需要轻量高效分类的环保科技应用
快速的经济和工业发展导致垃圾产量激增，亟需高效的垃圾管理策略以缓解环境污染和资源耗竭。借助计算机视觉技术的进步，本研究提出一种受像素分布学习启发的新方法，旨在提升自动化垃圾分类性能。该方法针对传统卷积神经网络（CNN）方法存在的计算复杂度高、对图像变化敏感等局限性进行改进。实验基于Kaggle Garbage Classification数据集展开，与现有模型对比，验证了像素分布学习在自动化垃圾分类中的有效性与高效性。
原文摘要 · Abstract (English)
The exponential growth in waste production due to rapid economic and industrial development necessitates efficient waste management strategies to mitigate environmental pollution and resource depletion. Leveraging advancements in computer vision, this study proposes a novel approach inspired by pixel distribution learning techniques to enhance automated garbage classification. The method aims to address limitations of conventional convolutional neural network (CNN)-based approaches, including computational complexity and vulnerability to image variations. We will conduct experiments using the Kaggle Garbage Classification dataset, comparing our approach with existing models to demonstrate the strength and efficiency of pixel distribution learning in automated garbage classification technologies.
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