Outdoor Check Stop HAZMAT Placard Detection Using Synthetic Images and YOLOv5-Small

Deep learning training is frequently supplemented with synthetic data. We propose a scheme to synthesize images of HAZMAT placards in outdoor environments (e.g., check stops) to train models for detection and classification. Our process has 3 levels of realism, with noise and other distortions, and can simulate day and nighttime images. We used our data to train YOLOv5-Small models and evaluated models over a test dataset of 4321 real outdoor check stop images containing 6738 placards across 16 classes. Respectively, the best models for similar and single-class placard groupings had 0.867 and 0.927 best half-precision test set [email protected]. Models run at 65 FPS on Nvidia’s AGX Xavier edge GPU single-board computer, and at 12 FPS on Nvidia’s Nano.