Road Damage: Berlin, 2020

1. Introduction The main idea of the project is to detect road damage in videos with a combination of artificial intelligence and image recognition. Each raw video file is provided with an associated geolocation file of the same name. This includes information about latitude and longitude, as well as the timestamp in milliseconds from the beginning of the video. A total of 3350 videos with a total range of 1 040 km were analysed in the area of the Berlin S-Bahn ring. The result file contains latitude and longitude (WGS84, EPSG:4326) of road damage in 4 damage categories: ‘Length crack’, ‘cross crack’, ‘crocodile crack’ and ‘stroke hole’. 2. Datasets To train the AI networks, the Road Damage Dataset 2020[1] was used. The data set is publicly available and can be viewed at the following link: Dataset 3. Methodology and models For the analysis of videos, the framework TensorFlow[2] was used. A model for object detection [3] was trained to detect road damage using Transfer Learning [4]. The results were summarised in a result JSON folder. Multiple detections were cleaned and the final locations of road damage were determined. 4. Source [1] Road Damage Challenge 2020 Dataset. [Online]. Available at: https://rdd2020.sekilab.global last accessed on 3 December 2020 [2] Martín A., Paul B., Jianmin C., Zhifeng C., Andy D., Jeffrey D.,... Xiaoqiang Zheng. (2016). »TensorFlow: a system for large-scale machine learning, in Proceedings of the 12nd USENIX conference on Operating Systems Design and Implementation (OSDI'16). USENIX Association, USA, 265-283 [3] Girshick, R., Donahue, J., Darrell, T., Malik, J. (2014). “Rich feature hierarchies for accurate object detection and semantic segmentation”, 2014 IEEE Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2014.81 [4] Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C. and Liu, C. (2018) “A survey on deep transfer learning”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11141 LNCS, pp. 270-279. doi: 10.1007/978-3-030-01424-7_27