关键词: 裂纹检测, 卷积神经网络, 显著图, 像素级检测, 实时检测

Abstract:

Crack detection is significant important for the safe operation of facilities and equipment. However, some problems exist in current crack detection methods：the data annotation process is cumbersome; the training process is time-consuming; and the detection speed is slow. In order to solve these problems, a weakly supervised crack detection algorithm is proposed. The algorithm uses the features extracted by the convolutional neural network to generate a saliency map containing the position information of the crack in the image, thus achieving crack detection. The experimental results show that the crack detection algorithm based on improved saliency map solves the difficulty in data annotation, and has a pixel-level detection effect while only need to learn image categories. The training time is short, only needs 23 minutes using two k80 GPUs, at the same time, achieving 52% average precision on the homemade data set and has a detection speed of 105 frames per second, which verifies the effectiveness of the algorithm.

Key words: crack detection, convolutional neural network, saliency map, pixel-level detection, real-time detection