2 posts tagged with "object-detection"

📌 Key Takeaways (TL;DR)

• Problem: Searching for tiny plates (30x15 pixels) across the raw 640x480 frame generates massive false positives from roadside signs and drops inference speed below 10 FPS.
• Solution: Designed a two-stage hierarchical architecture: broad vehicle detection (YOLOv3-tiny) crops the vehicle ROI, within which a fine-tuned YOLOv5 (lp_det.pt, conf=0.6) locates the license plate. Integrated OpenCV CSRT correlation filter tracking with a 15-frame redetection interval.
• Result: Eliminated 98% of background false positives and slashed GPU overhead by 80%, sustaining real-time 50+ FPS continuous tracking.

📌 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.