YOLOv5: A Comprehensive Technical Review of a Paradigm-Shifting Object Detector

Author(s)	Mr. HARISH M, Ms. RAKSHITA A, Ms. RAMYA RAMYA, Mr. ROHITH P G, Mr. SACHITH R
Country	India
Abstract	Real-time object detection is of paramount importance for robotics, autonomous vehicles, video analytics, and surveillance. This work presents the design, implementation, and evaluation plan of a practical real-time object detection system that balances a tradeoff between inference speed (in FPS) and quality of detection (in mAP). The system includes a lightweight detection backbone, optimized detection head, anchor-free YOLO family or lightweight DETR variants, and engineering optimizations such as model pruning, quantization, TensorRT/ONNX conversion, and multi-scale NMS. We detail the architecture, training regimen on COCO/transfer datasets, deployment strategy to edge GPUs, and experimental protocol for benchmarking latency, throughput, and accuracy. Modern state-of-the-art YOLO-family and efficient one-stage detectors provide very competitive speed-accuracy trade-offs in real-time applications, as revealed by results from the literature. Keywords: Feedback Classification, Natural Language Processing (NLP), Machine Learning (ML),
Keywords	Logistic Regression, College Help Desk, Automation, Sentiment Analysis.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.62737

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