Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust...Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.展开更多
文摘Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.
