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| Title | Energy-Efficient And Comprehensive Garbage Bin Overflow Detection Model Based On Spiking Neural Networks |
|---|---|
| ID_Doc | 23450 |
| Authors | Yang L.; Zha X.; Huang J.; Liu Z.; Chen J.; Mou C. |
| Year | 2025 |
| Published | Smart Cities, 8, 2 |
| DOI | http://dx.doi.org/10.3390/smartcities8020071 |
| Abstract | What are the main findings? This paper presents HERD-YOLO, a detection model for garbage bin overflow based on Spiking Neural Network s (SNNs). It not only achieves high accuracy in object detection but also significantly reduces energy consumption compared to traditional approaches based on artificial neural networks (ANNs). It also introduces the extensive Garbage Bin Status (GBS) dataset, comprising 16,771 images generated and augmented using techniques such as Stable Diffusion. This diverse dataset significantly enhances the model’s ability to generalize across various environmental conditions, including different weather and lighting scenarios. What is the implication of the main findings? The energy-efficient design of HERD-YOLO enables its deployment on resourceconstrained IoT devices, thereby making real-time waste management in smart cities more sustainable and cost effective. Enhanced with improved robustness and generalization capabilities, the model can accurately and promptly detect overflowing garbage bins under a wide range of realworld conditions. This ultimately facilitates smarter urban waste management and contributes to creating cleaner, healthier urban environments. Highlights: With urbanization and population growth, waste management has become a pressing issue. Intelligent detection systems using deep learning algorithms to monitor garbage bin overflow in real time have emerged as a key solution. However, these systems often face challenges such as lack of dataset diversity and high energy consumption due to the extensive use of IoT devices. To address these challenges, we developed the Garbage Bin Status (GBS) dataset, which includes 16,771 images. Among them, 8408 images were generated using the Stable Diffusion model, depicting garbage bins under diverse weather and lighting scenarios. This enriched dataset enhances the generalization of garbage bin overflow detection models across various environmental conditions. We also created an energy-efficient model called HERD-YOLO based on Spiking Neural Networks. HERD-YOLO reduces energy consumption by 89.2% compared to artificial neural networks and outperforms the state-of-the-art EMS-YOLO in both energy efficiency and detection performance. This makes HERD-YOLO a promising solution for sustainable and efficient urban waste management, contributing to a better urban environment. © 2025 by the authors. |
| Author Keywords | deep learning; diffusion model; garbage bin overflow detection; spiking neural networks |
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