traffic-sign-detection and Traffic-Sign-classifier-with-Deep-Learning
Detection and classification are complementary stages in a traffic sign understanding pipeline—the first tool identifies where signs are located in images, while the second classifies what type of sign each detected region contains.
About traffic-sign-detection
aarcosg/traffic-sign-detection
Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".
Implements comparative benchmarking of eight detector-backbone combinations (Faster R-CNN, R-FCN, SSD, YOLO V2) fine-tuned on the German Traffic Sign Detection Benchmark via transfer learning from COCO pre-trained weights. Built on TensorFlow's Object Detection API, the framework evaluates trade-offs across mAP, latency, FLOPs, memory footprint, and performance on variable input sizes, with pretrained models and evaluation notebooks provided for reproducibility.
About Traffic-Sign-classifier-with-Deep-Learning
neerajd12/Traffic-Sign-classifier-with-Deep-Learning
Classify traffic signs with Artificial neural networks
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