The Dynamic Image Analysis of Retaining Wall Crack Detection and Gap Hazard Evaluation Method with Deep Learning

Abstract

This study uses machine vision combined with drones to detect cracks in retaining walls in mountaineering areas or forest roads. Using the drone’s pre-collected images of retaining walls, the gaps in the wall are obtained as the target for sample data. Deep learning is carried out with neural network architecture. After repeated training of the module, the characteristic conditions of the crack are extracted from the image to be tested. Then, the various characteristics of the gap feature are extracted through image conversion, and the factors are analyzed to evaluate the danger degree of the gap. This study proposes a series of gap danger factor equations for the gap to analyze the safety of the detected gap image so that the system can judge the image information collected by the drone to assist the user in evaluating the safety of the gap. At present, deep learning modules and gap hazard evaluation methods are used to make suggestions on gaps. The expansion of the database has effectively improved the efficiency of gap identification. The detection process is about 20–25 frames per second, and the processing time is about 0.04 s. During the capture process, there will still be a few misjudgments and improper circle selections. The misjudgment rate is between 2.1% and 2.6%.

Review of Sensor-Based Subgrade Distress Identifications

“…Retaining wall crack Unmanned aerial vehicle (UAV) [92][93][94][95][96][97] Retaining wall slippage Light detection and ranging (LiDAR) [98][99][100][101] Anti-slide pile displacement…”

“…Deligiannakis et al [96] measured the retaining wall structure by an unmanned aerial vehicle equipped with laser radar and inferred the location and severity of cracks according to the point cloud data. Dong Han et al [97] combined machine vision with unmanned aerial vehicles (UAVs) to detect cracks in the retaining walls of mountain climbing areas or forest roads. Using the image of the retaining wall collected by UAVs in advance, the gap of the retaining wall is obtained as the target of sample data.…”

Design of Digital-Twin Human-Machine Interface Sensor with Intelligent Finger Gesture Recognition

“…In image pre-processing, RGB must first be converted to the HSV color space that is less affected by light [ 19 ] to facilitate the identification of skin color features of fingers and avoid most color noise. To effectively solve the RGB light source and color interference phenomenon obtained by camera photography, it is necessary to convert it into a more accurate and stable HSV color space, as shown in Equations (1)–(3) [ 19 ]. This will generate pseudo-colors, as shown in Figure 3 .…”

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

“…Machine learning methods have shown their high accuracy in detecting and monitoring defects in buildings (multiscale feature fusion method: 3ScaleNetwork deep convolutional neural network, local binary pattern, simple linear iterative clustering) [24], retaining walls (machine vision combined with drones) [25], bridges (improved YOLOv3 network, combining high-and low-resolution element images) [26], and pavements (model BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S)) [27]. There are quite a variety of methods for detecting and monitoring defects on the surface and inside concrete [28][29][30][31].…”