Infrared Clinical Enamel Crack Detector Based on Silicon CCD and Its Application: A High-Quality and Low-Cost Option

Enamel cracks generated in the anterior teeth not only affect the function but also the aesthetics of the teeth. Chair-side tooth enamel crack detection is essential for clinicians to formulate treatment plans and prevent related dental disease. This study aimed to develop a dental imaging system using a near-IR light source to detect enamel cracks and to investigate the relationship between anterior enamel cracks and age in vivo. A total of 68 subjects were divided into three groups according to their age: young, middle, and elderly. Near-infrared radiation of 850 nm was used to identify enamel cracks in anterior teeth. The results of the quantitative examination showed that the number of enamel cracks on the teeth increased considerably with age. For the qualitative examination, the results indicated that there was no significant relationship between the severity of the enamel cracks and age. So, it can be concluded that the prevalence of anterior cracked tooth increased significantly with age in the young and middle age. The length of the anterior enamel cracks tended to increase with age too; however, this result was not significant. The silicon charge-coupled device (CCD) with a wavelength of 850 nm has a good performance in the detection of enamel cracks and has very good clinical practicability.

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and Fext(0.8), which is a variant of F-score for crack detection, achieves 91.18%.

Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks

Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation with a deformable convolution operation and a deformable pooling operation. The idea is implemented on three different regular detectors, namely the Faster R-CNN, region-based fully convolutional networks (R-FCN), and feature pyramid network (FPN)-based Faster R-CNN. To examine the advantages of the proposed method, the results obtained from the proposed detector and corresponding regular detectors are compared. The results show that the addition of deformable modules improves the mean average precisions (mAPs) achieved by the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN for crack detection. More importantly, adding deformable modules enables these detectors to detect the out-of-plane cracks that are difficult for regular detectors to detect.

Concrete Crack Width Detecting System for Android Platform

In order to measure crack width accurately and automatically, an Android-based Automatic Crack Width Measuring System (ACWMS) has been developed, taking advantage of the high portability of Android devices. After capturing the image using mobile phone camera, the image is processed by image processing techniques, including graying, binaryzation, denoising and edge recognition. A specified algorithm is executed to calculate the crack width according to the provided edge data. Measurements has been done to each of the 10 cracks in the same concrete beam, using Samsung Galaxy S3 mobile phone and WYSX-40X Crack detector, respectively. Test result shows that the maximum crack width accuracy reaches 95.26%, which satisfies the construction needs. Therefore, this system can greatly improve the efficiency and accuracy during crack width measurement.