The authors constructed a dataset by combining GTSDB and GTSRB using seamless cloning and controlled image transformations.
Branched YOLOv2 with geometric features improves traffic sign recognition speed and accuracy.
The authors show that early termination for easy cases and geometric template matching reduce inference time and classification errors.
Academic
Arefeh Rezaei
K. N. Toosi University of Technology
Research Digest··2 min read
The authors extended YOLOv2 with intermediate prediction layers (branched architecture) to allow early exit for easy cases, and introduced geometric features from Bayesian segmentation to distinguish visually similar signs.
Why this paper
From K. N. Toosi University of Technology
In one line
Branched YOLOv2 with geometric features improves traffic sign recognition mAP from 0.680 to 0.713.
What we could check
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