Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.

Insurance CIOs are continually exploring the potential applications of AI, which resonate with their business needs. FREMONT, CA: Artificial intelligence (AI) is proliferating, and its ability to explore massive data sets and extract significant insights from them makes it an essential technology in the current market space. Thus, it isn't a surprise that insurance firms are also keen to utilize AI. Sources suggest that CIOs in the insurance sector will continue investing in AI technology to drive growth, revenue, streamline business operations and serve the customers and distribution partners in a better way. However, the main consideration lies in figuring out how AI can best address their business requirements.