A Road Defect Detection System Using Smartphones

Abstract

We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications.

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure

“…According to the above analysis, we believe that bonding distance measurement based on 2D images is practicable. In recent years, computer vision techniques [ 6 , 7 , 8 , 9 ], which represent an important research area in deep learning [ 10 , 11 ], have developed rapidly. Many computer-vision-based methods have been applied in various fields, including aero-engine blade defect detection [ 12 ], steel plate defect inspection [ 13 ], and visual measurements [ 14 ].…”