"AI-Driven Probabilistic Region-of-Interest Guided Optical Character Recognition (OCR) for Autonomous Traffic Sign Detection"

Engineering notes

The pipeline achieves a mean Average Precision (mAP) of 97.8% at an inference latency of 31 ms on CPU and 10.3 ms on GPU \u2014 a 78% latency reduction and 6.6 pp accuracy improvement over the full-frame scanning baseline.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

"Abstract\u2014Accurate and real-time road sign recognition is a safety-critical capability in autonomous driving systems, where detection latency directly impacts vehicle response and passenger safety. This paper presents an AI-driven, fully Python-based Optical Character Recognition (OCR) pipeline for autonomous traffic sign detection, incorporating a probabilistic Region of Interest (ROI) scanning strategy guided by historical spatial detection maps and a Long Short-Term Memory (LSTM) temporal predictor. Rather than processing full camera frames at high computational cost, the proposed system intelligently narrows its scanning focus to frame zones with the highest statistical likelihood of containing traffic signs, reducing the effective pixel evaluation area by up to 78% per frame. The pipeline achieves a mean Average Precision (mAP) of 97.8% at an inference latency of 31 ms on CPU and 10.3 ms on GPU \u2014 a 78% latency reduction and 6.6 pp accuracy improvement over the full-frame scanning baseline. A continuous AI self-improvement module employing Elastic Weight Consolidation (EWC) and memory replay enables incremental retraining from 94.2% to 98.4% validation accuracy across six rounds with negligible catastrophic forgetting."

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