Performance Analysis of Low-Computational Computer Vision Techniques for Recyclable Waste Identification.

Waste management on the low-resource setting is also a serious issue because of the shortage of infrastructure, and the lack of access to modern technologies, and the use of manual sorting of waste leads to environmental pollution and ineffective recycling. Although deep learning-based image classification has demonstrated the potential of automating waste, the majority of studies emphasize high-computation models, which cannot be used in resource-constrained environments. The research gap is a significant lack of systematic comparisons on both classification and computational efficiency of low-computational convolutional neural network (CNN) models. This study aim to examine the appropriateness of lightweight CNN models in the identification of recyclable wastes with a tradeoff between accuracy and resource consumption. Three low-computational CNN models, namely EfficientNet-Lite, MobileNetV2, and SqueezeNet, were trained and tested using publicly available datasets, such as TrashNet. Accuracy and F1-score were used to measure the classification performance, and latency, memory usage, and model size were used to measure computational efficiency. Findings show that EfficientNet-Lite had the best accuracy (92.4) and F1-score (92.0) but used more memory and inference time whereas MobileNetV2 provided a reasonable compromise between performance and efficiency. SqueezeNet was the lightest and the fastest model but with a lesser classification reliability. These results give practical recommendations on the design of AI-powered waste management in low-resource settings. This study helps to advance sustainable recycling efforts because it emphasizes the existence of the accuracy-latency-memory trade-offs of lightweight CNNs and informs practitioners and researchers on the need to use the right model whenever deploying lightweight CNNs to mobile, edge, or embedded applications in developing regions.