Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Urban Traffic Offense Detection: AI-Powered YOLOv8 Solutions for Smart Cities

Author(s) Ms. Akshata Mogare, Ms. Ankita Nathe, Ms. Ankita Nathe, Mr. Pawankumar Yadav, Ms. Vamini Rahangdale, Mr. Chandrashekhar lade, Mr. Vishal Rathod
Country India
Abstract Recent advancements in artificial intelligence and deep learning have enabled significant progress in computer vision applications, including object detection, optical character recognition (OCR), and image generation. This paper presents a deep learning–based framework that integrates state-of-the-art models with efficient computer vision techniques to achieve robust and real-time performance. The system leverages PyTorch, OpenCV, and PaddleOCR in combination with architectures such as YOLOv8, convolutional neural networks, and transformer-based models to deliver scalable solutions across multiple domains. The methodology involves structured data preprocessing, transfer learning, and model optimization techniques including pruning and quantization to balance speed and accuracy. Experimental evaluation demonstrates improved mean average precision (mAP) for object detection, reduced character error rate in OCR, and enhanced visual quality in generative tasks when compared
with conventional methods. The proposed framework highlights strong applicability in areas such as healthcare, agriculture, accessibility, and automated monitoring. The findings emphasize the potential of AI-driven solutions to address real-world challenges through accurate, efficient, and scalable computer vision systems.
Keywords Artificial Intelligence (AI), Deep Learning, Computer Vision, Object Detection, Optical Character Recognition (OCR), Generative AI, Real-Time Processing, Convolutional Neural Networks (CNN), Transformer Models, PyTorch, OpenCV, PaddleOCR, YOLOv8, Image Recognition, Automation.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-14
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5699
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