OCDBMamba:一个强大而高效的道路漏洞检测框架,具有全方位上下文和基于共识的边界建模
Feng Ling1, Yunfeng Lin1, Weijie Mao2
1College of Engineering, Lishui University, Lishui 323000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
通过整合全向上下文建模和共识驱动的边界选择,OCDBMamba增强了道路坑洞检测. 这一框架在具有挑战性的道路条件下提高了定位精度和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 道路坑洞检测对于基础设施维护和自动驾驶至关重要.
- 现有的方法与复杂的环境作斗争,导致虚假报警和边界不稳定.
研究的目的:
- 开发一个统一和高效的框架,用于强大的道路坑洞检测.
- 为了应对低对比度,阴影,水膜和背景纹理等挑战.
主要方法:
- 拟议的OCDBMamba框架与全方位通道选择性扫描 (OCS) 进行长距离的结构性暗示聚合.
- 引入双分支机构共识门 (DBCT) 以实现分支机构级协议和边界一致性.
主要成果:
- 在不同的数据集上实现了90.7%的mAP50和67.8%的mAP50:95.
- 证明了高精度 (0.905) 和回忆 (0.812) 与13.1 GFLOPs.
- 在关键指标中表现比YOLOv11n高5.4%和5.6%.
结论:
- 在各种道路条件下,OCDBMamba提供了稳定的定位和增强的坚固性.
- OCS和DBCT的协同作用验证了其在实际道路检查和感知方面的有效性.
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