融合深度图与卡尔曼滤波，提升车辆视频中坑洞检测与面积估计的精度和稳定性。
Robust Video-Based Pothole Detection and Area Estimation for Intelligent Vehicles with Depth Map and Kalman Smoothing
- 用ACmix模块和小目标检测头改进YOLOv8，增强小坑洞识别能力。
- 结合深度图与三角化像素法，实现坑洞面积精准估算，误差更小。
- 采用基于置信度与距离的卡尔曼滤波，使连续帧预测更稳定，适合车载应用。
道路坑洞威胁行车安全与舒适性，其检测与评估在自动驾驶等领域至关重要。传统视觉方法依赖距离先验构建几何模型，但受相机角度变化影响大，且常假设路面平坦，复杂环境下误差显著。本文提出一种融合单目深度估计与视频流的目标检测框架。首先，引入ACSH-YOLOv8模型，通过ACmix模块与小物体检测头提升小坑洞特征提取能力；其次，使用BoT-SORT算法进行坑洞跟踪，利用DepthAnything V2生成每帧深度图；基于深度图与标注结果，提出最小包围三角化像素（MBTP）方法进行坑洞面积估计；最后，设计基于置信度与距离的卡尔曼滤波（CDKF），保持连续帧间估计一致性。实验表明，ACSH-YOLOv8在AP(50)上达到76.6%，较YOLOv8提升7.6%；经CDKF优化后，预测结果更具鲁棒性，显著提升实际应用价值。
原文摘要 · Abstract (English)
Road potholes pose a serious threat to driving safety and comfort, making their detection and assessment a critical task in fields such as autonomous driving. When driving vehicles, the operators usually avoid large potholes and approach smaller ones at reduced speeds to ensure safety. Therefore, accurately estimating pothole area is of vital importance. Most existing vision-based methods rely on distance priors to construct geometric models. However, their performance is susceptible to variations in camera angles and typically relies on the assumption of a flat road surface, potentially leading to significant errors in complex real-world environments. To address these problems, a robust pothole area estimation framework that integrates object detection and monocular depth estimation in a video stream is proposed in this paper. First, to enhance pothole feature extraction and improve the detection of small potholes, ACSH-YOLOv8 is proposed with ACmix module and the small object detection head. Then, the BoT-SORT algorithm is utilized for pothole tracking, while DepthAnything V2 generates depth maps for each frame. With the obtained depth maps and potholes labels, a novel Minimum Bounding Triangulated Pixel (MBTP) method is proposed for pothole area estimation. Finally, Kalman Filter based on Confidence and Distance (CDKF) is developed to maintain consistency of estimation results across consecutive frames. The results show that ACSH-YOLOv8 model achieves an AP(50) of 76.6%, representing a 7.6% improvement over YOLOv8. Through CDKF optimization across consecutive frames, pothole predictions become more robust, thereby enhancing the method's practical applicability.
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