Road marking blur detection with drive recorder


Can we inspect the road condition at a low cost? City infrastructures, such as roads are very important for citizens to their city lives. Roads require constant inspection and repair due to deterioration, but it is expensive to do so with manual labor. Meanwhile, there are official city vehicles, especially garbage trucks that run through the entire area of a city every day and have cameras to record their driving. When we use these cameras, we can watch roads conditions anytime, anywhere. In our study, we focus on these cameras and attempt detecting the road damage, such as road marking blur. To achieve our goal, we explore the new system in this paper. This system adopts the object detection approach that is end-to-end learning and based on deep neural networks, which propose the blur region candidate and detect whether the road markings are blurred or not all at once. In our experiment, first, we obtain the drive recorder video from sanitation engineer and then annotate them. After annotation, we trained our models and calculate the mean average precision to evaluate our models. As a result, our model performs on our collected dataset.

2017 IEEE International Conference on Big Data (Big Data)
Makoto Kawano

My research interests include efficient machine learning for real-world deployment.