Waste Classification from Garbage Images Using Virions Optimization-Based Pooling Enhanced Deep Learning Framework

Waste Classification and management are crucial to improving the environment, with the ultimate goal of promoting economic recycling and environmental safety. Due to the vast diversity of waste, relying on manual Waste Classification and recycling waste products remains ineffective and costly. Advancements in Computer vision and Deep Learning technologies have been applied for Intelligent waste classification. However, the existing methods are incompetent to capture the discriminative features of waste from different classes and struggle with handling complex backgrounds. Consequently, this research proposes the Virions coordinated colony search optimization-based conditional learning with pooling enhanced convolutional neural network (VC2S-CPEnCN) to improve waste classification. Specifically, the proposed method exploits the Dense blockwise directional pattern (DeBDP) for extracting intrinsic features that provide better characterization of granular pixel regions. Further, the incorporation of conditional learning dynamically adapts the model to learn the complex relationships of waste objects even in varying lighting, occlusion, and background clutter conditions. In the proposed model, the application of T-max average pooling promotes adaptive feature retention without adding computational overhead. In addition, the hyperparameters are optimally tuned using the Virions coordinated colony search optimization (VC2S) that promotes faster convergence and improves the classification accuracy. Experimental results of the VC2S-CPEnCN are assessed using various metrics, exhibiting 96.78% accuracy, 96.83% F1-score, 96.97% precision, and 96.68% recall in the Garbage Classification dataset, outperforming the other conventional methods.