Abu-Raddaha Abdalmalek, El-Shair Zaid A, Rawashdeh Samir
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
J Imaging. 2024 Sep 14;10(9):227. doi: 10.3390/jimaging10090227.
Road conditions, often degraded by insufficient maintenance or adverse weather, significantly contribute to accidents, exacerbated by the limited human reaction time to sudden hazards like potholes. Early detection of distant potholes is crucial for timely corrective actions, such as reducing speed or avoiding obstacles, to mitigate vehicle damage and accidents. This paper introduces a novel approach that utilizes perspective transformation to enhance pothole detection at different distances, focusing particularly on distant potholes. Perspective transformation improves the visibility and clarity of potholes by virtually bringing them closer and enlarging their features, which is particularly beneficial given the fixed-size input requirement of object detection networks, typically significantly smaller than the raw image resolutions captured by cameras. Our method automatically identifies the region of interest (ROI)-the road area-and calculates the corner points to generate a perspective transformation matrix. This matrix is applied to all images and corresponding bounding box labels, enhancing the representation of potholes in the dataset. This approach significantly boosts detection performance when used with YOLOv5-small, achieving a 43% improvement in the average precision (AP) metric at intersection-over-union thresholds of 0.5 to 0.95 for single class evaluation, and notable improvements of 34%, 63%, and 194% for near, medium, and far potholes, respectively, after categorizing them based on their distance. To the best of our knowledge, this work is the first to employ perspective transformation specifically for enhancing the detection of distant potholes.
道路状况常常因维护不足或恶劣天气而恶化，这是导致事故的重要因素，而人类对诸如坑洼等突发危险的反应时间有限，使得情况更加严重。尽早发现远处的坑洼对于及时采取纠正措施（如减速或避开障碍物）以减轻车辆损坏和事故至关重要。本文介绍了一种新颖的方法，该方法利用透视变换来增强不同距离处的坑洼检测，尤其关注远处的坑洼。透视变换通过虚拟地拉近坑洼并放大其特征来提高坑洼的可见性和清晰度，鉴于目标检测网络通常对固定大小输入的要求（通常远小于相机捕获的原始图像分辨率），这一点尤为有益。我们的方法自动识别感兴趣区域（ROI）——道路区域——并计算角点以生成透视变换矩阵。该矩阵应用于所有图像和相应的边界框标签，增强数据集中坑洼的表示。当与YOLOv5-small一起使用时，这种方法显著提高了检测性能，在单类评估中，在交并比阈值为0.5至0.95时，平均精度（AP）指标提高了43%，在根据距离对近、中、远坑洼进行分类后，近、中、远坑洼的检测性能分别显著提高了34%、63%和194%。据我们所知，这项工作是首次专门采用透视变换来增强对远处坑洼的检测。
