Comparing Robust Image Retrieval and Waste Classification Using Deep Learning and Geometric Transformations

Smart waste classification and image retrieval are important for recycling, resource recovery, and circular-economy applications. This study presents a comparative deep learning framework for waste classification and content-based image retrieval (CBIR) under geometric transformations. Rather than proposing a single fused architecture, the study systematically investigates three key design factors: backbone architecture, classifier-head design, and loss-function selection. A custom waste image dataset comprising 1000 images across 10 waste categories was used throughout the experiments. The evaluated backbone architectures included ResNet18, ResNet34, and ResNet50, while the classifier heads consisted of a fully connected classifier, a DenseNet201-based classifier, and a VGG16-based classifier. In addition, three loss functions—cross-entropy, label smoothing, and ArcFace—were evaluated under identical experimental conditions. The results show that ResNet50 combined with a DenseNet201-based classifier and ArcFace achieved the highest classification performance, reaching a 98.4% accuracy and 98.3% F1-score. ArcFace also improved feature discriminability and enhanced retrieval effectiveness, with the best configuration achieving a 98.1% mean average precision (mAP). Robustness evaluation under rotation and scaling transformations demonstrated that the proposed framework maintained stable performance under geometric variations. Although ResNet50 achieved the highest accuracy, ResNet34 combined with a fully connected classifier and ArcFace provided a favorable balance between recognition performance and model complexity. These findings demonstrate that backbone depth, classifier-head design, and loss-function selection all play important roles in determining waste classification and retrieval performance.