This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in streetlevel panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also estimates many other roadlevel attributes, including curvature, roadside hazards, and the type of median. To support this, we incorporate taskspecific attention layers so the network can focus on the panorama regions that are most useful for a particular task. We evaluated our approach on a large dataset of real-world images from two US states. We found that incorporating additional tasks, and using a semi-supervised training approach, significantly reduced overfitting problems, allowed us to optimize more layers of the network, and resulted in higher accuracy.
|Title of host publication||Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018|
|Number of pages||9|
|State||Published - May 3 2018|
|Event||18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, United States|
Duration: Mar 12 2018 → Mar 15 2018
|Name||Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018|
|Conference||18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018|
|Period||3/12/18 → 3/15/18|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computer Science Applications