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<p>As urban waste issues become increasingly severe, traditional waste classification methods can no longer meet the demands for efficiency and accuracy. Deep learning technology has gradually become a key solution to this problem, yet improving classification accuracy, processing efficiency, and model adaptability in complex scenarios remains a challenge. This paper proposes a lightweight waste classification model, ResSeMo, based on multi-module collaborative optimization. The model combines ResNeXt’s multi-scale feature extraction, SENet’s channel attention mechanism, and MobileNetV3’s lightweight design to effectively improve classification accuracy, reduce computational overhead, and enhance robustness in complex backgrounds. Experimental results show that ResSeMo achieved 91.3% accuracy on the TrashNet dataset and 86.9% on the TACO dataset, maintaining high stability in disturbance experiments. Specifically, with Gaussian noise and random blur, ResSeMo’s accuracy dropped by only 4.1% on TrashNet and 6.4% on TACO. Compared to traditional models like VGG16, it demonstrates a clear advantage in anti-disturbance performance. This model not only performs excellently on standard datasets but also adapts to waste classification needs in complex environments, providing strong support for smart waste classification systems.</p>

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ResSeMo: deep convolutional neural network integration for high-accuracy waste classification and efficient processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Tao Liu,
  • Bingzheng Li,
  • Zihao Wang

摘要

As urban waste issues become increasingly severe, traditional waste classification methods can no longer meet the demands for efficiency and accuracy. Deep learning technology has gradually become a key solution to this problem, yet improving classification accuracy, processing efficiency, and model adaptability in complex scenarios remains a challenge. This paper proposes a lightweight waste classification model, ResSeMo, based on multi-module collaborative optimization. The model combines ResNeXt’s multi-scale feature extraction, SENet’s channel attention mechanism, and MobileNetV3’s lightweight design to effectively improve classification accuracy, reduce computational overhead, and enhance robustness in complex backgrounds. Experimental results show that ResSeMo achieved 91.3% accuracy on the TrashNet dataset and 86.9% on the TACO dataset, maintaining high stability in disturbance experiments. Specifically, with Gaussian noise and random blur, ResSeMo’s accuracy dropped by only 4.1% on TrashNet and 6.4% on TACO. Compared to traditional models like VGG16, it demonstrates a clear advantage in anti-disturbance performance. This model not only performs excellently on standard datasets but also adapts to waste classification needs in complex environments, providing strong support for smart waste classification systems.