Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion

Abstract

In this work, we propose a road pavement damage detection deep learning model based on feature points from a local minimum of grayscale. First, image blocks, consisting of the neighborhood of feature points, are cut from the image window to form an image block dataset. The image blocks are then input into a convolutional neural network (CNN) to train the model, extracting the image block features. In the testing process, the feature points as well as the image blocks are selected from a test image, and the trained CNN model can output the feature vectors for these feature image blocks. All the feature vectors will be combined to a composite feature vector as the feature descriptor of the test image. At last, the classifier of the model, constructed by a support vector machine (SVM), gives the classification as to whether the image window contains damaged areas or not. The experimental results suggest that the proposed pavement damage detection method based on feature-point image blocks and feature fusion is of high accuracy and efficiency. We believe that it has application potential in general road damage detection, and further investigation is desired in the future.

Keywords

• local minimum of grayscale
• feature fusion
• pavement damage detection
• deep learning