In order to improve the detection level of bridge cracks,and solve the time-consuming and laborious problem in manual detection and the parameters to be set manually in traditional image processing methods,an improved bridge crack detection algorithm was proposed based on GoogLeNet. Firstly,a large-scale bridge crack Retinex-Laplace-Histogram equalization(RLH)dataset was constructed for model training and testing. Secondly,based on the original GoogLeNet model,the inception module was improved by using the normalized convolution kernel,three improved schemes were used to modify the beginning of the network,the seventh and later inception layers were removed,and a bridge crack feature image classification system was established. Finally,the sliding window was used to accurately locate the cracks and the lengths and widths of the cracks were calculated by the skeleton extraction algorithm. The experimental results show that compared with the original GoogLeNet network,the improve-GoogLeNet network increased the recognition accuracy by 3. 13%, and decreased the training time to the 64. 6% of the original one. In addition,the skeleton extraction algorithm can consider the trend of the crack,calculate the width more accurately,and the maximum width and the average width can be calculated. In summary,the classification and measurement method proposed in this paper have the characteristics of high accuracy,fast speed,accurate positioning and accurate measurement.

To overcome the weakness of traditional detection approaches for regional staff density, and meanwhile obtain the regional staff density information better from Probe Request (PR) frame sended from mobile phones with the enabled Wireless-Fidelity (Wi-Fi), a detection probability model of regional staff density based on Wi-Fi devices was proposed. Firstly, the average PR frame time intervals of common mobile phones were obtained by experiment, which could provide a guidance for setting some parameters of probability model. Secondly, according to the IEEE 802.11 standard and Wi-Fi channel attributes, a Wi-Fi detector's mathematical model was built. Finally, reasonable values were assigned to these parameters on the basis of specific environment, and the detection probability of detector was simulated. The theoretical analysis and simulation results show that the proposed mathematical model can reflect the detection situations for staff density.

To resolve the irrationality in the definition and measurement of intuitionistic fuzzy entropy, a new axiomatic definition for intuitionistic fuzzy entropy was proposed, and a new measuring formula was structured. Firstly, the existing differences in research of axiomatic definition for intuitionistic fuzzy entropy were analyzed, its defects and insufficiency were also pointed out. Secondly, an improved axiomatic definition for intuitionistic fuzzy entropy and a calculation formula of intuitionistic fuzzy entropy were proposed. Finally, the new formula was compared with the existing formulas for intuitionistic fuzzy entropy by examples. The results of the example analysis show that, the proposed entropy formula can reflect better the uncertainty and fuzziness of intuitionistic fuzzy sets, and the capability to discriminate the uncertainty of intuitionistic fuzzy sets is stronger.

Skew document images often appears when it is captured by image acquisition devices such as cameras or scanners, which may induce recognition mistakes by Optical Character Recognition (OCR) software. The paper proposed an optimized method for the skew detection of document images, and its objective function is the image projection grating slit width. The document image angle is the inverse of the projection angle, when the corresponding projection grating slit width is the largest. The detection range is expanded and the detection speed is increased by the grating line width function. The amount of calculation in detection is decreased by preliminary projection on equispaced rows and back projection. The detection precision is improved by dichotomy. In the experiments document images where a few illustrations were used, and the skew detection results show the proposed method is of high efficiency and robustness.