A Lightweight Knowledge-Distilled CNN for Real-Time Waste Classification on Edge Devices

The exponential rise in solid waste generation, coupled with inefficient manual sorting practices, has intensified the need for automated waste classification systems that can operate reliably within resource-constrained Internet of Things (IoT) infrastructures. While deep convolutional neural networks (CNNs) have demonstrated strong performance in waste classification tasks, their substantial computational and memory requirements render them unsuitable for deployment on low-power edge devices commonly used in smart bin applications. To address this gap, we propose a lightweight CNN architecture trained via knowledge distillation to enable accurate, real-time waste classification on edge hardware. We first benchmarked thirteen pretrained CNN architectures, including VGG, ResNet, DenseNet, MobileNetV2, InceptionV3, Xception, EfficientNet, and NASNet variants, to identify the most effective teacher model. ResNet152 achieved the highest classification accuracy of 98.02\% and was subsequently employed to guide the training of a compact student network through a distillation-based learning framework. The proposed student model, trained under teacher supervision, achieved a classification accuracy of 93.35%, a substantial improvement over the 86.44% obtained when trained from scratch without distillation, while maintaining a significantly smaller parameter footprint suitable for embedded deployment. Experimental results confirm that the distilled model preserves much of the teacher’s discriminative capability while drastically reducing inference latency and memory overhead. The classifier categorizes waste into biodegradable, non-biodegradable, and background classes, making it directly applicable to intelligent smart bin systems for automated, real-time waste sorting in smart city and environmental monitoring applications, contributing toward sustainable and scalable waste management infrastructure.