This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/mask pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.
翻译:本文介绍向SHREC 2022 轨迹提交的关于坑洞和路面裂缝探测的方法,对道路表面的语义分解总共进行了7次不同的测试,其中6次来自参与者,加一个基线方法,所有方法都利用深学习技术及其性能都使用同样的环境进行测试(即:一个单一的Jupyter笔记本),一套由3836个语义分解图像/图像对体组成的培训套件,以及用最新深度照相机收集的797个RGB-D视频短片提供给参与者,然后对鉴定组中的496对图像/图像对子、测试组中的504对成对和最后8个视频短片进行评估,对结果的分析以图像分解的定量指标和视频剪片的质量分析为基础,参与情况和结果显示,对设想非常感兴趣,在这方面使用RGB-D数据仍然具有挑战性。