Developing a system capable of recognizing objects and individuals in images through the application of convolutional neural networks (Comprehensive Journal of Science, 2025)
The aim of this study was to design, test, and fully analyze a combined real-time system using convolutional neural networks (CNNs) with the ability to achieve general object detection and single (person) re-identification in unconstrained images simultaneously. The system proposed follows a hybrid two-stream design: the former stream uses an improved version of YOLOv5 to quickly and precisely detect multi-objects, and the latter stream is based on the modified ResNet-50 backbone that was trained using the triplet and ArcFace losses to learn highly discriminative identity representations. Other improvements are Squeeze-andExcitation attention blocks, widespread data augmentation, ImageNet training transfer, mixed-precision training and distributed, multi-GPU optimization. Strict testing using massive benchmark tasks (COCO, CelebA, cross-dataset), showed an average Average Precision (mAP 0.5:0.95) of 0.62 on object detection and a top-1 identification rate of 92 percent, which is a 817 percent improvement over baseline models. It was shown to high-level adversarial resistance, variations in illumination, and partial occlusions with real-time inference time of less than 200 ms on consumer-grade GPUs. Extensive studies regarding ablation and demographic equity also confirmed the role of every component and limited bias. Such findings make the suggested framework a very practical and deployable tool in a large number of applications, such as intelligent surveillance, assistive robotics, humanrobot interaction, forensic analysis, and smart environments. This work in the end provides a resultant reproducible, scalable and state-of-the-art pipeline that can be used as a strong building block to unified visual recognition systems in the next generation.
Keywords: Convolutional Neural Networks, Object Detection, Person Re-identification, YOLOv5, ResNet-50, Transfer Learning, Metric Learning, Triplet Loss, ArcFace, Face Recognition, Computer Vision, Real-time Inference, Fairness, Robustness.×