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In recent years, deep learning approaches have shown superior performance in car lane detection compared to traditional methods. However, the computational demands of deep learning algorithms often hinder their real-time application in autonomous vehicles. To address this issue, this study proposes a lane detection technique that combines the power of deep learning with real-time requirements. A lightweight convolutional neural network model is employed as a feature extractor, trained on a dataset of 16 x 64 pixel tiny image patches. Fast inference is achieved using a non-overlapping sliding window technique, and lane boundaries are modeled by fitting a polynomial to the predictions. The proposed technique is evaluated on the KITTI and Caltech datasets, demonstrating satisfactory performance. Additionally, we integrate the detector into our autonomous vehicle's localization and planning system, achieving a real-time processing speed of 28 frames per second on a CPU with an image resolution of 768 x 1024, meeting the requirements for self-driving cars.
Upon purchasing this project online, you will receive recorded video tutorials, comprehensive documentation, complete frontend (HTML, CSS, JavaScript) and backend (Python) codes, reports, PPTs, and datasets, all sent automatically to your email. For further support, contact our technical team at +91 8088605682. Please note that once payment is made, no refunds will be issued under any circumstances.
Receive fully functional and tested code tailored to car lane detection, implemented using Python with popular frameworks like TensorFlow or OpenCV.
Get detailed documentation, including reports, PPTs, and datasets for research papers, sent automatically to your email upon purchase.
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This is one of the best IEEE Machine Learning project ideas for final-year students. The project includes complete frontend (HTML, CSS, JavaScript) and backend (Python) codes, along with detailed explanations to ensure thorough understanding. We also provide content for your report and IEEE paper publication, making it a commercially viable solution for applications in autonomous vehicles and beyond.