Publication Details


AI tool with active learning for detection of rural roadside safety features

Type: conferencePapers

Author(s): Yi, Hong; Bizon, Chris; Borland, David; Watson, Matthew; Satusky, Matthew; Rittmuller, Robert; Radwan, Randa; Srinivasan, Raghavan; Krishnamurthy, Ashok

Pages: 5317-5326

Publisher: IEEE

Url: https://ieeexplore.ieee.org/document/9671360/

Publication Date: Dec-2021

Isbn: 978-1-6654-3902-2

Doi: 10.1109/BigData52589.2021.9671360

Abstract: Roadway safety, especially in rural areas, is one of the most critical components in transportation planning. In collaboration with North Carolina Department of Transportation (NCDOT), UNC Highway Safety Research Center (HSRC), and DOT Volpe National Transportation Systems Center, UNC Renaissance Computing Institute (RENCI) developed a roadside feature detection solution leveraging multiple convolutional neural networks. The solution used an iterative active learning (AL) computer vision model training pipeline integrated into an AI tool to detect safety features such as guardrails and utility poles in geographically distributed NC rural roads. We utilized transfer learning by adopting the Xception neural network architecture [1] as the feature extraction backbone which was then used in an iterative AL process supported by a web-based annotation tool. The annotation tool not only allowed for the collection of annotations through an iterative AL process for multiple safety features, it also enabled visual analysis and assessment of model prediction performance in the geospatial context. AL techniques were used to direct human annotators to label images that would most effectively improve the model aimed at minimizing the number of required training labels while maximizing the model’s performance. The iterative AL process combined with a common feature extraction backbone allowed fast model inference on millions of images in the AL sampling space. This enabled a rapid transition between AL rounds while also reducing the computing requirements for each round. Model feature extraction weights were then fine-tuned in the last round of AL to obtain the best accuracy. Since only about 2.7% of 2.6 million unlabeled images in the AL sampling space contain guardrails, there is a significant class imbalance problem that must be addressed in our AL sampling strategies for the guardrail classification model. In this paper, we present our AI tool processing pipeline and methodology and discuss our AL results and future work. Our AI tool can be used to detect roadside safety features and be extended to also locate them for assessing roadside hazards.

Conference name: 2021 IEEE International Conference on Big Data (Big Data)

Conference_proceedings_title: 2021 IEEE International Conference on Big Data (Big Data)