A High-Accuracy Crack Defect Detection Based on Fully Convolutional Network Applied to Building Quality Inspection Robot

Wentao Zhang; Haoran Kang; Wenhui Wang; Yangtao Ge; Haiou Liao; Rui-Jun Yan; I-Ming Chen; Zhenzhong Jia; Jing Wu

10.1109/ICARM54641.2022.9959693

2022-07-09

IEEE-ARM

978-1-6654-8307-0

2022 International Conference on Advanced Robotics and Mechatronics (ICARM)

63-68

2022年7月9-11日

桂林

Crack defect detection plays a significant role in the industry, especially in construction. This paper proposes a high accuracy detection method based on the improved fully convolutional network (FCN), which can be used in the building quality inspection robot. After building the map, an algorithm based on topology is applied to recognize the contour of the house, plan the path and take photos. The up-sampling method is improved based on the existing network structure, and a crack detection model is built. A Deepcrack dataset is used to train the model and tested with 100 images in various scenes. Experimental results with mean-IoU of 48.27% and accuracy of 92.81% indicate that the proposed detection model can accurately detect the cracks at the pixel-wise level.

Industries Mechatronics Service robots Network topology Image edge detection Buildings Inspection

其他

人工提交

南方科技大学

Wentao Zhang,Haoran Kang,Wenhui Wang,et al. A High-Accuracy Crack Defect Detection Based on Fully Convolutional Network Applied to Building Quality Inspection Robot[C],2022:63-68.