Concrete Crack Region Detection Based on High-Dimensional Image Feature Compressed Sensing

With the simple and naive feature representation, the concrete crack region detection is sensitive for the illumination and background disturbances. Through multifold image region feature extraction models, massive and rich texture features of image region can be obtained, thereby leading to a better crack detecting accuracy. However, with the high dimensional features, the resultant crack region detection would be suffered from a huge data storage and computational burden. To address this problem mentioned above, a novel crack region detector based on high dimensional image feature compressed sensing was presented. Specifically, based upon Johnson-Lindenstrauss lemma, a good discrimination between crack and non-crack region samples can be achieved using a fewer compressed region features. Then, least square support vector machine was utilized for efficiently separating the compressed crack features and non-crack ones. Plenty experiments on practically collected concrete images demonstrate that the training efficiency of developed crack detector is more than 150 times faster than that of high-dimensional features. Meanwhile, our crack region detecting accuracy is 90.3%, and the crack detecting recall rate can reach 91.2%, which is superior to other compared crack detection methods.