  This paper summarizes the design, experiments and results of our solution to
the Road Damage Detection and Classification Challenge held as part of the 2018
IEEE International Conference On Big Data Cup. Automatic detection and
classification of damage in roads is an essential problem for multiple
applications like maintenance and autonomous driving. We demonstrate that
convolutional neural net based instance detection and classfication approaches
can be used to solve this problem. In particular we show that Mask-RCNN, one of
the state-of-the-art algorithms for object detection, localization and instance
segmentation of natural images, can be used to perform this task in a fast
manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of
50% on the task of detection and classification of different types of damages
in real-world road images acquired using a smartphone camera and our average
inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti
graphic card. The code and saved models for our approach can be found here :
https://github.com/sshkhr/BigDataCup18 Submission
