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.

In order to improve image retrieval performance, this paper proposed a new image retrieval algorithm based on motif and color features. The color image edge gradient was detected, and by means of edge gradient image transform, a motif image was obtained. Adopting the gravity center of motif image as the datum point, the distances of all points were calculated to the datum point to get the motif center distance histogram. The all motifs of the motif image were projected in four different directions to get motif projective histogram. Color image was uniformly quantized into 64-color space from RGB space to obtain the color histogram. The above three histograms described image features for image retrieval. The experimental results show that the algorithm has high precision and recall.