2 posts tagged with "super-resolution"

📌 Key Takeaways (TL;DR)

• Problem: Cropped license plates from dashcam footage often measure under 40x20 pixels with heavy H.264 macroblocking; standard bicubic interpolation fails to resolve character contours.
• Solution: Integrated EDSR_x4 deep residual networks (EDSR_x4.pb) that omit batch normalization to preserve dynamic range, combined with temporal multi-frame image stacking across 3 to 5 consecutive frames.
• Result: Quadrupled plate pixel density and sharpened stroke edges, lifting downstream EasyOCR accuracy on Korean characters from 32% to over 91%.

📌 Key Takeaways (TL;DR)

• Problem: Low-resolution dashcam footage suffers from severe vehicle vibration, nighttime backlighting, and heavy motion blur, making direct license plate recognition and lane violation tracking extremely error-prone.
• Solution: Designed a unified 6-stage vision architecture: Canny/Hough lane extraction -> YOLOv3 vehicle detection -> polygon IoU lane violation calculation -> YOLOv5 plate localization -> EDSR_x4 super-resolution -> EasyOCR temporal voting.
• Result: Constructed an end-to-end Python pipeline achieving robust vehicle tracking and lane boundary monitoring even under degraded dashcam conditions.