IoT-based Visual Crack Detection Based on Multi-scale Self-Attention Neural Network

IoT devices offer an efficient solution for collecting visual data for inspections, including identifying potential cracks in infrastructure. Drones can capture precise images or videos, which are then transmitted to the cloud for processing or analyzed using fog computing nodes. Edge computing allows on-site data analysis, optimizing performance and reducing latency. Recently, Visual Crack Detection (VCD) using low-cost cameras has gained attention in Structural Health Monitoring (SHM) for maintenance and damage identification. Crack Detection (CD) algorithms face challenges from brightness changes, shadows, and complex backgrounds. Deep Learning (DL) networks have been developed to improve crack identification, but most use local attributes and pixel correlations. A proposed multi-scale convolutional network inspired by Self-Attention (SA) combines local and global attributes for more robust crack detection. The performance of this multi-scale convolutional network has been tested on some well-known crack datasets and evaluated against state-of-the-art approaches. The test results on the well-known Crack500 dataset outperforms the existing approaches.