Name
Abstract | ||
|---|---|---|
Industrial procedures can be inefficient in terms of time, money and consumer satisfaction. the rivalry among businesses' gradually encourages them to exploit intelligent systems to achieve such goals as increasing profits, market share, and higher productivity. The property casualty insurance industry is not an exception. The inspection of a roof's condition is a preliminary stage of the damage claim processing performed by insurance adjusters. When insurance adjusters inspect a roof, it is a time consuming and potentially dangerous endeavor. In this paper, we propose to automate this assessment using RGB imagery of rooftops that have been inflicted with damage from hail impact collected using small unmanned aircraft systems (sUAS) along with deep learning to infer the extent of roof damage (see Fig. I). We assess multiple convolutional neural networks on our unique rooftop damage dataset that was gathered using a sUAS. Our experiments show that we can accurately identify hail damage automatically using our techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/AIPR.2017.8457946 | 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) |
Keywords | Field | DocType |
roof damage assessment,deep learning,property casualty insurance industry,inspection,damage claim processing,insurance adjusters,hail impact,sUAS,hail damage,convolutional neural networks,rooftop damage dataset,roof condition,RGB imagery,small unmanned aircraft systems | Intelligent decision support system,Convolutional neural network,Computer science,Property insurance,Exploit,Risk analysis (engineering),Roof,Artificial intelligence,Deep learning,Market share,Profit (economics) | Conference |
ISSN | ISBN | Citations |
1550-5219 | 978-1-5386-1236-1 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahshad Mahdavi Hezaveh | 1 | 0 | 0.34 |
| Christopher Kanan | 2 | 310 | 25.31 |
| Carl Salvaggio | 3 | 5 | 4.83 |