Region-based Distress Classification of Road Infrastructures via CNN without Region Annotation

This paper presents region-based distress classification of road infrastructures via convolutional neural networks (CNN) without region annotation. Although CNNs are often used for classification tasks recently, CNNs trained from images which contain unnecessary regions cannot perform precise classification. Distress images of road infrastructures contain various unnecessary objects other than the target distress. Although target regions should be provided in order to achieve high performance, it is a time-confusing task for engineers. This paper focuses on removing unnecessary objects in the images without region annotation via an object detection method. Especially, by using a pre-trained object detection model with distress images of road infrastructures, distress regions in the images are detected automatically. Our proposed CNN trained from the obtained distress regions realizes precise distress classification.