Crack Detection on a Retaining Wall with an Innovative, Ensemble Learning Method in a Dynamic Imaging System

Abstract

In this study, an innovative, ensemble learning method in a dynamic imaging system of an unmanned vehicle is presented. The feasibility of the system was tested in the crack detection of a retaining wall in a climbing area or a mountain road. The unmanned vehicle can provide a lightweight and remote cruise routine with a Geographic Information System sensor, a Gyro sensor, and a charge-coupled device camera. The crack was the target to be tested, and the retaining wall was patrolled through the drone flight path setting, and then the horizontal image was instantly returned by using the wireless transmission of the system. That is based on the cascade classifier, and the feature comparison classifier was designed further, and then the machine vision correlation algorithm was used to analyze the target type information. First, the system collects the target image and background to establish the samples database, and then uses the Local Binary Patterns feature extraction algorithm to extract the feature values for classification. When the first stage classification is completed, the classification results are target features, and edge feature comparisons. The innovative ensemble learning classifier was used to analyze the image and determine the location of the crack for risk assessment.

Combining Nadir, Oblique, and Façade Imagery Enhances Reconstruction of Rock Formations Using Unmanned Aerial Vehicles

“…By splitting the original data into 3 × 3 chunks, the processing time of data set 4 was reduced by approximately two-thirds in the experiment. Despite these constraints, the incorporation of façade photographs can provide significant extra value to topographic data to accommodate various UAV applications [47].…”

“…A factor that requires further research is the influence of the subsequent survey products, especially the production of the orthomosaic. In previous studies where façade photographs were included, the final products were usually either a point cloud, 3-D model, or an orthomosaic in a local planar coordinate system, rather than a real-world projected coordinate system [32], [47], [48]. Conventionally, the orthomosaic produced for topographic survey applications would be composed mainly of nadir aerial images with the normal vectors approximately perpendicular to the ground surface [43], [49].…”

Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+

“…Kang et al [7] used Fast RCNN to extract the crack area in the panoramic image with an anchor box and processed the 2 of 15 image in the area to obtain the length and width of the crack. However, the noise existing in the anchor box still affected the subsequent crack boundary extraction [8,9]. At present, many studies on crack segmentation based on semantic segmentation models such as FCN [10][11][12], U-Net [13][14][15][16][17], PSPNet [18], and Deeplab series [19,20] have emerged, which verify the effectiveness of semantic segmentation models for crack extraction.…”

A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

“…Image segmentation refers to dividing an image into several non-overlapping areas, based on features such as grayscale, color, texture, and shape, and making these features appear similar in the same area, but obvious differences appear between the different areas. The image segmentation method is as follows [7][8][9][10]:…”