A UAV Based Concrete Crack Detection and Segmentation Using 2-Stage Convolutional Network with Transfer Learning

Abstract

This study explores a non-destructive testing (NDT) method for crack detection using a two-stage convolutional neural network (CNN) model, incorporating a combination of AlexNet and YOLO models through transfer learning. Crack detection is pivotal for assessing structural integrity and ensuring timely maintenance interventions. The developed model was rigorously tested in simulated environments and through physical experimentations with the use of a UAV to evaluate its effectiveness. A 2-stage model, based on AlexNet and YOLO, was developed for crack classification and segmentation. The developed model leveraged transfer learning to address limitations from traditional CNN models. A known dataset was used to evaluate the developed model, benchmarking it against other models. The classification network achieved an accuracy rate exceeding 90%, while the segmentation network successfully identified and delineated cracks in 85.71% of the images. Finally, the developed model was deployed using a UAV to perform crack detection and segmentation in a controlled environment. These results underscore the model's proficiency in both detecting and segmenting structural cracks, highlighting its potential as a reliable tool for enhancing the maintenance and safety of architectural structures. Doi: 10.28991/HIJ-2024-05-03-010 Full Text: PDF

Keywords

• computer vision
• 2-stage cnn
• crack detection
• crack segmentation
• transfer learning
• unmanned aerial vehicles (uav).