Benchmarking Robots for Everyday Environments: From Lab Experiments to Real-World Operations
研究通过三年实地测试，使用跨学科框架评估公共环境中部署的机器人，解决实验室指标与实际需求之间的差距。
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Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case
This work addresses the challenges of scarce labeled data and weakly expressed, low-salience product attributes in applications such as digital product passports by proposing a two-step verification generative information extraction framework that integrates pretrained language models (PLMs) with large language models (LLMs). The approach first employs a PLM for initial candidate extraction and then leverages a locally deployable open-source LLM—such as those in the Llama family—for secondary verification and error correction, substantially improving extraction accuracy for sparse and weakly expressed entities. Experimental results demonstrate that the proposed framework enhances generalization capability and enables medium-scale models to approach the performance of much larger models, all while preserving data privacy and maintaining computational efficiency. The method has been successfully integrated into a demonstration system tailored for digital product passports.
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Transfer learning-based method for automated ewaste recycling in smart cities
This study addresses the low efficiency and insufficient accuracy in electronic waste sorting by proposing, for the first time, a transfer learning–based approach tailored to few-shot e-waste classification. Specifically, the authors introduce an intelligent sorting method built upon fine-tuned AlexNet, enhanced with data augmentation, hyperparameter optimization, and an SGD with Momentum optimizer (learning rate 3e-4). Evaluated on a small-scale dataset comprising 12 smartphone categories across six brands, the method achieves a classification accuracy of 98%. The results demonstrate a significant reduction in sorting errors and validate the feasibility of high-precision automated classification under resource-constrained conditions, offering a promising pathway for integrating artificial intelligence into circular economy initiatives.
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Battery detection of XRay images using transfer learning
This study addresses the challenge of automatic detection, localization, and type identification of lithium-ion batteries in X-ray images by proposing a two-stage transfer learning approach. First, a YOLOv5m model is fine-tuned on a large-scale dataset of electronic devices; subsequently, the adapted model is transferred to the X-ray domain to jointly detect and classify three battery types—prismatic, pouch, and cylindrical. The proposed method achieves a detection accuracy of 94%, representing a 5% improvement over the original pretrained model, while maintaining a real-time inference speed of 22 milliseconds per frame. This approach effectively balances high accuracy with computational efficiency, offering a practical and scalable solution for intelligent battery sorting systems.
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Don't waste SAM
This study addresses the performance degradation of the general-purpose segmentation model SAM in real-world waste scenarios, where occlusion, deformation, transparency, and background clutter pose significant challenges. For the first time, the authors systematically evaluate and fine-tune SAM’s generalization capability across three waste datasets—Zerowaste, TACO, and TrashCan 1.0. Building upon the SAM-ViT-H architecture and employing a targeted supervised fine-tuning strategy, the approach substantially improves segmentation accuracy on complex waste objects. Experimental results demonstrate that the fine-tuned model achieves IoU gains exceeding 30 on both Zerowaste and TACO, and lags only 1.44 behind the state-of-the-art on TrashCan 1.0, significantly outperforming existing methods overall. These findings validate SAM’s effectiveness and potential as a foundational model for waste segmentation tasks.
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Benchmarking Robots for Everyday Environments: From Lab Experiments to Real-World Operations
研究通过三年实地测试，使用跨学科框架评估公共环境中部署的机器人，解决实验室指标与实际需求之间的差距。
0 citationsRead paper
Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case
This work addresses the challenges of scarce labeled data and weakly expressed, low-salience product attributes in applications such as digital product passports by proposing a two-step verification generative information extraction framework that integrates pretrained language models (PLMs) with large language models (LLMs). The approach first employs a PLM for initial candidate extraction and then leverages a locally deployable open-source LLM—such as those in the Llama family—for secondary verification and error correction, substantially improving extraction accuracy for sparse and weakly expressed entities. Experimental results demonstrate that the proposed framework enhances generalization capability and enables medium-scale models to approach the performance of much larger models, all while preserving data privacy and maintaining computational efficiency. The method has been successfully integrated into a demonstration system tailored for digital product passports.
0 citationsRead paper
Transfer learning-based method for automated ewaste recycling in smart cities
This study addresses the low efficiency and insufficient accuracy in electronic waste sorting by proposing, for the first time, a transfer learning–based approach tailored to few-shot e-waste classification. Specifically, the authors introduce an intelligent sorting method built upon fine-tuned AlexNet, enhanced with data augmentation, hyperparameter optimization, and an SGD with Momentum optimizer (learning rate 3e-4). Evaluated on a small-scale dataset comprising 12 smartphone categories across six brands, the method achieves a classification accuracy of 98%. The results demonstrate a significant reduction in sorting errors and validate the feasibility of high-precision automated classification under resource-constrained conditions, offering a promising pathway for integrating artificial intelligence into circular economy initiatives.
0 citationsRead paper
Battery detection of XRay images using transfer learning
This study addresses the challenge of automatic detection, localization, and type identification of lithium-ion batteries in X-ray images by proposing a two-stage transfer learning approach. First, a YOLOv5m model is fine-tuned on a large-scale dataset of electronic devices; subsequently, the adapted model is transferred to the X-ray domain to jointly detect and classify three battery types—prismatic, pouch, and cylindrical. The proposed method achieves a detection accuracy of 94%, representing a 5% improvement over the original pretrained model, while maintaining a real-time inference speed of 22 milliseconds per frame. This approach effectively balances high accuracy with computational efficiency, offering a practical and scalable solution for intelligent battery sorting systems.
0 citationsRead paper
Don't waste SAM
This study addresses the performance degradation of the general-purpose segmentation model SAM in real-world waste scenarios, where occlusion, deformation, transparency, and background clutter pose significant challenges. For the first time, the authors systematically evaluate and fine-tune SAM’s generalization capability across three waste datasets—Zerowaste, TACO, and TrashCan 1.0. Building upon the SAM-ViT-H architecture and employing a targeted supervised fine-tuning strategy, the approach substantially improves segmentation accuracy on complex waste objects. Experimental results demonstrate that the fine-tuned model achieves IoU gains exceeding 30 on both Zerowaste and TACO, and lags only 1.44 behind the state-of-the-art on TrashCan 1.0, significantly outperforming existing methods overall. These findings validate SAM’s effectiveness and potential as a foundational model for waste segmentation tasks.
0 citationsRead paper