Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time‐consuming and difficult. This paper proposed a deep‐learning‐based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real‐world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color‐differences, and nonuniform illumination on the concrete surface.

Apparent quality of concrete is one of the important indexes of engineering quality assessment, occupying a very important position in the quality of engineering. As an important evaluation index of the apparent quality of concrete, the detection of cracks is particularly important. The traditional detection method mainly relies on the inspection personnel to carry out on-site inspection, which has low efficiency and large environmental limitations. In this paper, a method of crack image recognition based on convolution neural network is proposed, which can realize the automatic recognition of concrete surface cracks, and the recognition accuracy rate is as high as 93.7%.

Concrete exterior quality is one of the important metrics in evaluating construction project quality. Among the defects affecting concrete exterior quality, bughole is one of the most common imperfections, thus detecting concrete bughole accurately is significant for improving concrete exterior quality and consequently the quality of the whole project. This paper presents a deep learning-based method for detecting concrete surface bugholes in a more objective and automatic way. The bugholes are identified in concrete surface images by Mask R-CNN. An evaluation metric is developed to indicate the scale of concrete bughole. The proposed approach can detect bugholes in an instance level automatically and output the mask of each bughole, based on which the bughole area ratio is automatically calculated and the quality grade of the concrete surfaces is assessed. For demonstration, a total of 273 raw concrete surface images taken by mobile phone cameras are collected as a dataset. The test results show that the average precision (AP) of bughole masks is 90.8%.