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The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

ABSTRACT In nature, complex carbohydrates are rarely found as pure isolated polysaccharides. Instead, bacteria in competitive environments are presented with glycans embedded in heterogeneous matrices such as plant or microbial cell walls. Members of the Bacteroidota phylum thrive in such ecosystems because they are efficient at extracting nutrients from complex substrates, secreting consortia of synergistic enzymes to release metabolizable sugars. Carbohydrate-binding modules (CBMs) are used to target enzymes to substrates, enhancing reaction rate and product release. Additionally, genome organizational tools like polysaccharide utilization loci (PULs) ensure that the appropriate set of enzymes is produced when needed. In this study, we show that the soil bacterium Chitinophaga pinensis uses a PUL and several CBMs to coordinate the activities of enzymes targeting two distinct polysaccharides found in fungal cell walls. We describe the enzymatic activities and carbohydrate-binding behaviors of components of the fungal cell wall utilization locus (FCWUL), which uses multiple chitinases and one β-1,3-glucanase to hydrolyze two different substrates. Unusually, one of the chitinases is appended to a β-glucan-binding CBM, implying targeting to a bulk cell wall substrate rather than to the specific polysaccharide being hydrolyzed. Based on our characterization of the PUL’s outer membrane sensor protein, we suggest that the FCWUL is activated by β-1,3-glucans, even though most of its enzymes are chitin-degrading. Our data showcase the complexity of polysaccharide deconstruction in nature and highlight an elegant solution for how multiple different glycans can be accessed using one enzymatic cascade. IMPORTANCE We report that the genome of the soil bacterium Chitinophaga pinensis encodes three multi-modular carbohydrate-active enzymes that work together to hydrolyze the major polysaccharide components found in fungal cell walls (FCWs). The enzymes are all encoded by one polysaccharide utilization locus and are co-expressed, potentially induced in the presence of β-1,3-glucans. We present biochemical characterization of each enzyme, including the appended carbohydrate-binding modules that likely tether the enzymes to a FCW substrate. Finally, we propose a model for how this so-called fungal cell wall utilization locus might enable C. pinensis to metabolize both chitin and β-1,3-glucans found in complex biomass in the soil.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.