论文标题
使用深层学习的道路损害检测
Road Damage Detection using Deep Ensemble Learning
论文作者
论文摘要
道路损伤检测对于维护道路至关重要，该道路传统上是使用昂贵的高性能传感器进行的。随着技术的最新进展，尤其是在计算机视觉方面，现在可以检测和分类不同类型的道路损失，这可以促进有效的维护和资源管理。在这项工作中，我们提出了一个合奏模型，用于有效地检测和分类道路损坏，我们已将其提交给IEEE Bigdata杯挑战赛2020年。我们的解决方案利用了一个最先进的对象探测器，即您只看一次（YOLO-V4），该探测器（YOLO-V4）是对来自CZECH，日本和印度的各种类型的道路损害的训练。我们的合奏方法通过多个不同的模型版本进行了广泛的测试，并且能够在测试1数据集中达到0.628的F1分数和测试2数据集的0.6358。
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, we present an ensemble model for efficient detection and classification of road damages, which we have submitted to the IEEE BigData Cup Challenge 2020. Our solution utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damages from Czech, Japan and India. Our ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.
