DeepHybridNet: A Novel CNN-Transformer Architecture for Robust Object Recognition and Categorization

Having profound implications for autonomous systems, intelligent surveillance, and human-computer interaction, object detection and classification of images are one of the principal challenges in computer vision. Using the vast benchmark datasets of PASCAL VOC and ImageNet, this study introduces a deep learning system for object recognition and classification. The model employs a hybrid structure combining Vision Transformers (ViT) and ResNet-50 for extracting global context and spatial features. On the more diversified and complex PASCAL VOC dataset, the pre-trained weights of ImageNet are transferred for the purpose of generalization and speed of convergence. By thorough testing on PASCAL VOC 2007 and 2012 datasets, the model outperformed baseline models like Faster R-CNN and YOLOv5 by 2.7% and 1.9%, respectively, in terms of mean Average Precision (mAP) at 83.4% on VOC 2007 and 82.1% on VOC 2012. With 78.9% top-1 classification accuracy, ImageNet-based training significantly improved the system discriminative ability for 1,000 object categories. Greater robustness to occlusion and class imbalance were achieved with increased data augmentation, optimized focus loss, and improved attention modules.