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New framework uses YOLO and RT-DETR for depth-aware pothole detection

Researchers have developed a depth-aware pothole detection framework that utilizes RGB-D sensors for improved accuracy. The study compared five different architectures, including YOLOv8n, YOLOv8nSeg, YOLOv9t, RTDETRL, and RTDETRX, on a custom dataset. YOLOv8nSeg demonstrated the highest mean average precision (mAP) and the most accurate depth estimation, while YOLOv8n offered the fastest inference times. AI

IMPACT This research advances computer vision techniques for infrastructure monitoring, potentially improving road maintenance and safety.

RANK_REASON Academic paper detailing a new computer vision framework and model comparison. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework uses YOLO and RT-DETR for depth-aware pothole detection

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Academic paper detailing a new computer vision framework and model comparison. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md Monjurul Ahsan Prodhan, Md Nour Hossain ·

    Depth-Aware Pothole Detection Using YOLO and RT-DETR at the Edge

    arXiv:2608.27633v1 Announce Type: cross Abstract: Pothole detection and its severity measurement is still an important challenges in urban infrastructure management, where late maintenance directly contributes to vehicle damage, road accidents, and escalating repair costs. Existi…