• Dwiti Pandya
• Rikita Gohil

Developing self-driving cars is an important foundation for the development of intelligent transportation systems. It is challenging to detect lanes quickly and accurately due to a variety of complex noise, so the main aim is to develop a collection of image processing techniques that give accurate results quickly. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane detection algorithm barriers for smart traffic are proposed. This paper includes an intelligent lane and vehicle recognition technique that utilizes a collection of distinct photos and applies the results to a video stream. The Hough transform is chosen as the most effective beeline detection technique, and the Canny algorithm is chosen as the edge detection technique. The ROI is defined to decrease noise for accurate rise and to increase processing speed to satisfy the real-time need. For detecting the vehicle, to provide fast implementation and smooth real-time update of the vehicles nearby we are implementing the YOLOV7 algorithm. We use real time videos and the TuSimple dataset to perform simulations for the proposed algorithm.

Lane detection, Vehicle detection, systematic literature review, geometric modeling, deep learning, machine learning.

"An Intelligent Lane and Object Detection using YOLO V7 algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f886-f889, May-2024, Available :http://www.jetir.org/papers/JETIR2405599.pdf

"An Intelligent Lane and Object Detection using YOLO V7 algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf886-f889, May-2024, Available at : http://www.jetir.org/papers/JETIR2405599.pdf