https://ijmcr.in/index.php/ijmcr/issue/feed 2025-04-01T11:26:53+00:00 Tapasya Vishwa editor@ijmcr.in Open Journal Systems <p>IJMCR is an international journal which provides a plateform to scientist and researchers all over the world for the dissemination of knowledge in computer science , mathematical sciences and related fields. Origional research papers and review articles are invited for publication in the field of Computer science, Software engineering, Programming, Operating system, Memory structure, Compilers, Interpretors, Artificial intelligence, Complexity, Information storage and Retrival, Computer system organization and Communication network, Processor architectures, Image and Speech processing, Pattern recognition and Graphics, Database management, Data structure, Applications, Information system, Internet, Multimedia Information system, User Interface, Human Computer Interface, Computing methodologies, Automation, Robotics and related fields. Similarly, origional research papers and review articles of Pure mathematics, Applied mathematics, Mathematical sciences and related fields can also be considered for the publication in the journal.</p> https://ijmcr.in/index.php/ijmcr/article/view/909 2025-04-01T11:26:53+00:00 Ripunjay Singh ripunjaysingh2104114@dei.ac.in Sarthak Goyal sarthakgoyal2104118@dei.ac.in Shivam Agarwal shivamagarwal2104119@dei.ac.in Divyansh Saxena saxenadivyansh47@gmail.com Subho Upadhyay shubhome2k10@dei.ac.in

<p>With an emphasis on performance under various ambient illumination circumstances this paper explores the potency of YOLOv8 variants for vehicle and license plate detection. The suggested method will capture entire video frames, identify areas of interest with cars, and feed those regions into two distinct, pre-trained YOLOv8 models—one for license plate recognition and the other for vehicle detection. To make the photos easier for the Tesseract OCR engine to read, they are pre-processed using the OpenCV and Pillow libraries to make the images brighter and higher DPI. The four YOLOv8 models can be paired for vehicle and license plate identification tasks to produce sixteen possible combinations. We evaluate the performance of the chosen YOLOv8 combinations under various ambient light intensity levels (measured in lux) after they have been selected using TOPSIS analysis. Finding the most reliable model combinations that provide precise license plate and vehicle detection in the variety of illumination situations found in real-world settings is the goal of this evaluation.</p>

2025-04-01T11:26:53+00:00 ##submission.copyrightStatement##