Ship structures are subjected to corrosion inevitably in service. Existed image-based methods are influenced by the noises in images because they recognize corrosion by extracting features. In this paper, a novel method of image-based corrosion recognition for ship steel structures is proposed. The method utilizes convolutional neural networks (CNN) and will not be affected by noises in images. A CNN used to recognize corrosion was designed through fine-turning an existing CNN architecture and trained by datasets built using lots of images. Combining the trained CNN classifier with a sliding window technique, the corrosion zone in an image can be recognized.
Crack is one of important damages on real concrete surface. The visual inspection that depends on inspectors, a primary method to detect cracks, is laborious and time-consuming in practical operation. Fortunately, image processing techniques make the crack detection more automated to some extent. However, the extracting of features is certainly necessary when image processing techniques detect crack in an image. As a result, the usage of image processing techniques is also limited, since images taken on real concrete surface are influenced by some noises caused by lighting, blur, and so on. In this paper, a method of convolutional neural networks-based crack detection for real concrete surface was proposed. The convolutional neural networks (CNNs) can learn the features of images automatically instead of extracting features, and therefore the CNNs will not be influenced by the noises. A convolutional neural network (CNN) used to detect crack was designed through fine-turning an existed CNN architecture. In order to train the CNN, image datasets needed be built firstly. A large number of images were taken from real concrete surface using a smartphone, cropped into small images, classified and labeled. A CNN classifier used to detect crack can be obtained by training the CNN according to those built datasets. Through integrating the trained CNN classifier into a smartphone application, the detection of crack in an image can be implemented automatically. The results illustrate that the proposed method shows high accuracy and robust performance and can indeed detect crack on real concrete surface.
In order to monitor the strain of concrete caused by freeze-thaw (F-T) cycles, two novel types of white light interferometer (WLI) sensor are designed and tested. The first type of sensor is poured the whole saturated concrete cylinder which coiled optical fiber with epoxy for encapsulation, and the second type coat neutral silicone sealant on the surface of cylinder, where enwound with optical fiber only. Each of the type was conducted on two sensors, the sensor of the first type was named W-sensor, and the sensor of the second type was named S-sensor. The comparison of the two novel types of sensor was conducted based on the test results, and the test result showed that though all of the two types of sensor can monitor the variation of strain with the process of F-T cycles, however, the type of S-sensor is more stability and reasonable.
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