A Two-Stage Deep Learning Framework for Enhanced Waste Detection and Classification

With the rapid modernization progress over the past decades, waste classification has become increasingly important as cities worldwide seek to implement more sustainable waste management practices. Traditional manual sorting methods are labor-intensive, prone to inaccuracies, and hard to scale, driving the need for automated, efficient solutions. Although deep learning techniques, recognized for their ability to process complex hierarchical data, have emerged as potential aids, their successful implementation is often hampered by the challenging task of gathering diverse, large-scale, high-quality waste image datasets, leading to possible overfitting and model bias. This study proposes an innovative, two-stage waste detection framework that first identifies the bounding box of waste items, then classifies them into one of six primary categories, effectively addressing the inherent issues with previous methodologies by optimally utilizing available data and reducing overfitting and bias. Trained and evaluated on the comprehensive TACO and WaRP waste datasets, our model has demonstrated superior performance relative to existing methods, underscoring its promise as a scalable, accurate, and efficient solution for waste classification, thus offering exciting prospects for further research and practical applications in sustainable waste management.