Author: Denis Avetisyan
Researchers have developed a new approach to automatically designing efficient object detection models capable of identifying waste on resource-constrained devices like microcontrollers.
This work presents TrashDet, an iterative neural architecture search framework optimized for accurate and efficient waste detection on edge computing platforms using the TACO dataset.
Deploying accurate object detection on resource-constrained edge devices remains a significant challenge due to the inherent trade-off between model complexity and computational efficiency. This paper introduces ‘TrashDet: Iterative Neural Architecture Search for Efficient Waste Detection’, a novel framework that automatically designs a family of efficient and accurate detectors specifically for waste recognition. Through iterative hardware-aware neural architecture search and a unique weight-sharing strategy, TrashDet achieves state-of-the-art performance on the TACO dataset while substantially reducing energy consumption and latency compared to existing TinyML detectors. Could this approach unlock scalable and sustainable solutions for intelligent waste management and broader edge computing applications?
The Challenge of Real-Time Waste Recognition
The promise of truly efficient waste management hinges on the ability to automatically identify and categorize refuse, yet current automated detection systems frequently falter when tasked with real-time operation and deployment on devices with limited processing power. Many existing solutions, while achieving high accuracy in controlled settings, demand substantial computational resources-making them unsuitable for integration into the very infrastructure they aim to improve, such as sensors on collection vehicles or within smart bins. This disparity between analytical capability and practical implementation presents a critical obstacle; the need for lightweight, responsive algorithms is paramount to enabling widespread, scalable, and cost-effective smart waste management initiatives across diverse and often unpredictable environments.
Conventional object detection models, frequently achieving high accuracy in identifying waste materials, rely on complex architectures and substantial computational resources. This intricacy translates to significant processing demands, making their deployment on edge devices – such as low-power sensors or embedded systems – largely impractical. The models’ appetite for processing power and memory often exceeds the capabilities of these resource-constrained platforms, hindering real-time analysis and autonomous operation. Consequently, the potential for widespread, efficient waste management through smart technologies is limited by the inability to perform accurate detection directly at the source, necessitating data transmission to more powerful, centralized servers – a process that introduces latency and increases infrastructure costs.
The difficulty in deploying smart waste management systems beyond controlled environments significantly hinders broader sustainability efforts. Current technological limitations create a bottleneck, as the computational demands of effective object detection often exceed the capabilities of low-power, resource-constrained devices commonly found in diverse public spaces. This disparity restricts implementation to areas with robust infrastructure, leaving many communities – particularly those in developing regions or with limited budgets – unable to benefit from automated waste detection. Consequently, the potential for data-driven optimization of waste collection routes, reduction of landfill overflow, and promotion of circular economy principles remains largely unrealized across a substantial portion of the global landscape, impeding progress towards more efficient and environmentally responsible waste management practices.
TrashDet: An Evolutionary Path to Efficient Detection
TrashDet utilizes an iterative evolutionary search strategy to identify optimal neural network architectures. This process begins with a randomly initialized population of network configurations, which are then evaluated based on both accuracy and computational efficiency metrics. Networks demonstrating superior performance are selected as ‘parents’ and undergo operations-such as mutation and crossover-to generate a new generation of candidate architectures. This cycle of evaluation and reproduction is repeated over multiple generations, progressively refining the population towards architectures exhibiting a desirable balance between predictive capability and resource usage. The automated nature of this search eliminates the need for manual architecture engineering, allowing for the discovery of novel and potentially high-performing network designs.
The computational expense of Neural Architecture Search (NAS) is substantially reduced by employing an ‘Accuracy Predictor’ during candidate network evaluation. This predictor, trained on a dataset of known architectures and their corresponding performance metrics, estimates the accuracy of novel architectures without requiring full training and validation. By substituting the time-consuming process of complete evaluation with a prediction, the search process can rapidly assess a larger number of candidate architectures, significantly accelerating the discovery of optimal network configurations. The predictor’s accuracy is crucial; therefore, it is regularly refined using data generated from fully trained networks to minimize prediction error and maintain a strong correlation between predicted and actual performance.
Population Passthrough is a key component of the TrashDet architecture search, functioning by directly carrying forward a predefined percentage of the highest-performing network architectures from one generation to the next. This mechanism bypasses the typical evolutionary operations – mutation and crossover – for these elite candidates, guaranteeing their continued presence in the population. By preserving proven architectures, Population Passthrough mitigates the risk of losing beneficial traits during search and provides a stable foundation for further optimization. This strategy demonstrably accelerates the convergence of the evolutionary process and improves the overall efficiency of network architecture discovery, as computational resources are not wasted re-discovering previously successful configurations.
Optimized Architectures for Edge Performance
The automated architecture search employed during the development of TrashDet identified both MobileNet and ResNet as effective foundational structures for feature extraction. This process systematically evaluated various network configurations, ultimately determining that implementations leveraging these backbones provided an optimal balance between computational efficiency and detection accuracy. The selection of these architectures facilitated the creation of a model capable of performing object detection tasks with reduced resource requirements, a key consideration for edge deployment scenarios.
The TrashDet networks achieved a mean Average Precision at IoU=0.5 (mAP50) of 19.5 on the TACO dataset, establishing a new state-of-the-art performance level. This result indicates superior object detection accuracy compared to all previously published baseline models evaluated on the same TACO dataset. The mAP50 metric assesses the precision of bounding box detections, with a higher value signifying improved detection capability and reduced false positives across the dataset’s image set.
TrashDet achieves a mean Average Precision at IoU=0.5 (mAP50) score competitive with larger models despite a substantially reduced parameter count. Specifically, TrashDet utilizes only 30.5 million parameters to achieve its mAP50 score, whereas the AltiDet-m model requires 85.3 million parameters to reach its performance level. This demonstrates a significant level of model compression is possible without sacrificing detection accuracy, offering benefits for deployment on resource-constrained edge devices.
When deployed on the MAX78002 microcontroller, the TrashDet-ResNet configuration demonstrates substantial energy efficiency gains. Measured energy consumption is reported at 7,525 μμJ. This represents an 87.9% reduction in energy use compared to the ai87-fpndetector, which consumes 62,001 μμJ under the same deployment conditions. These results indicate a significant improvement in power efficiency for edge-based object detection tasks using the TrashDet-ResNet architecture on the specified hardware.
Performance benchmarks demonstrate a substantial reduction in processing time using the TrashDet-ResNet network. Specifically, TrashDet-ResNet achieves a latency of 26.7 milliseconds, representing a 78.2% decrease compared to the 122.6 milliseconds recorded by the ai87-fpndetector. This reduction in latency indicates a significant improvement in real-time processing capabilities, allowing for faster object detection and analysis on edge devices.
Evaluations conducted on the MAX78002 microcontroller demonstrate that the TrashDet-MBNet configuration achieves a mean Average Precision at IoU=0.5 (mAP50) of 93.3. This represents a substantial performance improvement of 10.2 percentage points over the ai87-fpndetector, which recorded a mAP50 of 83.1 under the same testing conditions. This data indicates a significant gain in object detection accuracy for TrashDet-MBNet when deployed on the specified hardware.
![This approach achieves a 2.8x reduction in model size while maintaining comparable performance, or a 2.0% accuracy improvement for models of similar size, as demonstrated on the TACO dataset [taco2020].](https://arxiv.org/html/2512.20746v1/imgs/topright.png)
Beyond Waste: Expanding the Horizon of Real-Time Perception
The development of TrashDet showcases a powerful new approach to deploying object detection models on resource-constrained devices. Rather than relying on manually designed architectures or computationally expensive training methods, the system leverages an evolutionary search algorithm to automatically optimize the model for both accuracy and efficiency. This process effectively ‘evolves’ a neural network specifically tailored for edge deployment, identifying configurations that minimize size and computational demands without significantly sacrificing performance. The success of this technique demonstrates the potential of evolutionary algorithms to overcome the challenges of deploying complex AI models in real-world applications, paving the way for more intelligent and responsive edge devices.
The adaptable nature of the TrashDet framework extends far beyond waste management, offering a powerful tool for a diverse range of real-time vision applications. Its core principles – automated model optimization for resource-constrained devices – prove particularly valuable in fields like environmental monitoring, where rapid analysis of camera feeds can track deforestation, wildlife populations, or pollution levels. Similarly, the framework holds significant promise for advancing robotic navigation, enabling robots to efficiently process visual data for obstacle avoidance, path planning, and object recognition in dynamic environments. This ability to tailor object detection models for efficient edge deployment signifies a versatile platform with broad implications for any system requiring immediate, on-site visual analysis, ultimately fostering more responsive and autonomous technologies.
The development of effective edge-based artificial intelligence necessitates a careful equilibrium between computational accuracy and processing efficiency. Deploying complex, highly accurate models on resource-constrained devices often proves impractical due to limitations in power, memory, and bandwidth. Conversely, prioritizing efficiency at the expense of accuracy can render the system unreliable or ineffective for its intended purpose. Recent research underscores that successful edge AI design isn’t simply about achieving the highest possible accuracy, but rather about identifying the optimal balance – a point where sufficient precision is maintained while adhering to the strict limitations of the hardware. This requires innovative approaches to model optimization, such as neural network pruning, quantization, and knowledge distillation, all geared towards creating lean, powerful AI solutions capable of operating effectively in real-world, often unpredictable, environments.
The pursuit of efficient neural architectures, as demonstrated by TrashDet, echoes a fundamental principle of good design: elegance born from deep understanding. This work isn’t simply about shrinking a model; it’s about finding the right model for the task and the hardware. As David Marr observed, “Representation is the key to intelligence.” TrashDet exemplifies this, meticulously crafting a representation-a neural network-specifically suited for waste detection on edge devices. By prioritizing hardware-aware optimization and iterative search, the framework achieves a harmonious balance between accuracy and efficiency, ensuring the model whispers insights from the TACO dataset rather than shouting with computational demands. This emphasis on streamlined representation isn’t merely a technical detail; it’s a commitment to intelligent design.
Beyond the Bin: Charting Future Directions
The pursuit of efficiency in object detection, as demonstrated by this work, inevitably circles back to fundamental questions of representation. The TACO dataset, while valuable, represents a curated simplification of the world’s visual noise. Future iterations must confront the inherent messiness of real-world waste streams-occlusion, varied lighting, and the sheer diversity of discarded objects. A truly elegant solution will not merely detect waste, but understand it, differentiating between recyclable materials and contaminants with minimal computational burden.
The current emphasis on neural architecture search, while promising, risks becoming a localized optimization. The framework’s effectiveness is inextricably linked to the search space itself. Expanding this space-perhaps through the incorporation of neuromorphic computing principles or spiking neural networks-could unlock genuinely radical improvements in energy efficiency and model size. A good interface is invisible to the user, yet felt; similarly, a powerful model should operate with deceptive simplicity.
Ultimately, the true measure of success lies not in achieving incremental gains on benchmark datasets, but in fostering a more sustainable relationship with the materials that surround us. Every change should be justified by beauty and clarity. The challenge, then, is to move beyond the ‘detection’ problem and towards a holistic system capable of facilitating responsible waste management at scale.
Original article: https://arxiv.org/pdf/2512.20746.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-28 09:39