Road crack detection is an important task for road safety and road maintenance. In the past, people made use of manual detection methods and tried to use computer vision to detect crack. The most prominent feature in recent years is the use of deep learning. However, there is no good deep learning method for road crack detection under noise. This challenge is faced bravely. First, a noise crack dataset is proposed, consisting of multiple noise crack images which is called NCD. Then, an adaptive bilateral filtering algorithm is developed, which can reduce the influence of noise. Finally, a new crack detection network with two new modules is designed. In the end, it is found that all the parts have promoting effects on crack detection under noise. Compared with other state‐of‐the‐art methods, this method performs better, especially in road crack detection under noise. When evaluating the well‐known crack500 test set, ODS F‐measure of 0.628 is achieved. Besides, this method is also evaluated in another five datasets. Significantly, ODS F‐measure of 0.545 is achieved, 4.0% higher than state‐of‐the‐art on GAPs384.
Smart face identification is widely used in smart city and smart healthcare. However, smart face identification technology is susceptible to envirnmental factor, such as illumination, mask, and expression. In order to fully extract facial feature information, we fuse an improved local binary pattern (LBP) and the histogram of oriented gradients (HOG) to extract the texture and detailed features on the face. The 2DPCA + PCA is used to reduce the dimensionality of the extracted features. The 2DPCA sloves the issue that the model is too complex when the feature dimension is very high. The feature reduction reduces the calculation scale and increases the calculation speed. Finally, experimental results on ORL and Yale face databases show that the feature extraction based on the fusion of improved LBP and HOG complement with each other. Compared with other recognition algorithms, the improved algorithm has higher recognition and identification rate.
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