An Advanced Hybrid Deep Learning Framework for Automated Waste Classification and Detection

This paper suggests a dual-algorithmic model that integrates a dual-stream deep classification network with an advanced object detection module based on DD-YOLO to address the inefficiencies of improper garbage sorting and environmental degradation. Ineffective waste disposal results in resource mismanagement and environmental degradation; hence, automating garbage detection and classification has become inevitable. The proposed system incorporates two complementary feature extractors, one of ConvNeXt and one of InceptionResNetV2, which are both designed to extract diverse spatial information and contextual hints from input images. The combined feature map is processed through a new fusion method incorporating spatial attention modules and additional convolutional layers to strengthen discriminative capability. Meanwhile, the DD-YOLO module detects and localizes trash objects under difficult backgrounds with further detection layers and more advanced anchor mechanisms. Large-scale experiments on a diverse dataset-further enriched with rotation, noise injection, and scaling transformations-validate that the unified framework obtains resilient performance with excellent accuracy for classification and object localization. Training involves adaptive learning rate schedules, stochastic regularization, and label smoothing to guarantee convergence and prevent overfitting. This scalable architecture is particularly amenable for deployment in smart city infrastructures and IoT-supported waste management systems. The dual-algorithmic method thus supports environmental sustainability and operational efficiency through accurate waste identification and real-time detection.