Computer vision based asphalt pavement segregation detection using image texture analysis integrated with extreme gradient boosting machine and deep convolutional neural networks. (15th June 2022)

• Record Type: Journal Article
• Authors: Hoang, Nhat-Duc; Tran, Van-Duc
• Is Part Of: Measurement. Volume 196(2022)
• Publication Date: 2022-06-15
• Subjects: Computer vision -- Asphalt pavement segregation -- Machine learning -- Deep learning -- XGBoost -- Texture analysis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111207
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Propose a computer vision method for detecting asphalt pavement segregation. Employ image texture analysis for characterizing pavement surface condition. Extreme gradient boosting machine and deep neural network are used for classification. Attractive repulsive center-symmetric local binary pattern is used for texture computation. XGBoost has achieved the best detection accuracy with accuracy rate = 0.95. Abstract: Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95).