English(EN) Depth-Aware Pothole Detection Using YOLO and RT-DETR at the Edge

新框架使用YOLO和RT-DETR进行深度感知坑洼检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员开发了一个深度感知坑洼检测框架，该框架利用RGB-D传感器提高准确性。研究在自定义数据集上比较了五种不同的架构，包括YOLOv8n、YOLOv8nSeg、YOLOv9t、RTDETRL和RTDETRX。YOLOv8nSeg在平均精度均值（mAP）和深度估计准确性方面表现最佳，而YOLOv8n的推理速度最快。 AI

影响这项研究推进了用于基础设施监控的计算机视觉技术，有望改善道路维护和安全。

排序理由详细介绍新计算机视觉框架和模型比较的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Md Monjurul Ahsan Prodhan, Md Nour Hossain · 2026-08-31 04:00

基于 YOLO 和 RT-DETR 的边缘端深度感知坑洼检测

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…

本文如何被排名

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基于 YOLO 和 RT-DETR 的边缘端深度感知坑洼检测

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