alen-smajic/Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
This project helps autonomous driving engineers analyze and compare the real-time performance of object detection models. You input video footage from a vehicle, and it outputs videos with detected objects like cars, pedestrians, and traffic signs highlighted with bounding boxes. This is ideal for researchers or engineers working on computer vision systems for self-driving cars.
No commits in the last 6 months.
Use this if you need to benchmark and visualize how different object detection algorithms perform in real-time on driving video data.
Not ideal if you're looking for a plug-and-play solution for commercial autonomous driving systems without diving into model training and configuration.
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MIT
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Last pushed
Mar 16, 2021
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