bharatmahaur/ComparativeStudy
Code for paper: "Road object detection: a comparative study of deep learning-based algorithms" https://link.springer.com/article/10.1007/s11042-022-12447-5
This project helps evaluate and compare different deep learning models for detecting objects on roads, such as vehicles, pedestrians, or traffic signs, from camera images. It takes raw image data and produces trained models capable of identifying and outlining these objects, along with metrics on their performance. Researchers and engineers working on autonomous driving or intelligent transportation systems would find this useful.
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Use this if you are developing or evaluating road object detection systems and need to compare various state-of-the-art deep learning algorithms.
Not ideal if you are looking for an out-of-the-box solution for a specific real-world application without any deep learning expertise.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Oct 31, 2022
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