recepayddogdu/Object_Detection_Classification_-_Ford_Otosan_Intern_P2
Development of Deep Learning algorithms for Drivable Area Segmentation, Lane Segmentation, Traffic Sign Detection and Classification with data collected and labeled by Ford Otosan.
Implements SegNet for pixel-wise lane segmentation using pooling indices from VGG16-based encoders, and YOLOv4 for traffic sign detection with subsequent classification on 40+ Turkish highway-specific sign classes. Converts Ford Otosan's JSON annotation format to training-ready masks and YOLO coordinate formats, supporting both PyTorch and TensorFlow backends for end-to-end autonomous driving perception pipelines.
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Feb 18, 2022
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