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 (CNNs). The solution uses 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. The RENCI team utilized transfer learning by adopting the Xception neural network architecture 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 allows for the collection of annotations through an iterative AL process for multiple safety features, but it also enables 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 had to be addressed in our AL sampling strategies for the guardrail classification model. Our AI tool can be used to detect roadside safety features and be extended to also locate them for assessing roadside hazards.