A new rail inspection method based on deep learning using laser cameras

Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.

Download Full-text

An Automated Inspection Method for the Steel Box Girder Bottom of Long-Span Bridges Based on Deep Learning

IEEE Access ◽

10.1109/access.2020.2994275 ◽

2020 ◽

Vol 8 ◽

pp. 94010-94023

Author(s):

Dalei Wang ◽

Yiquan Zhang ◽

Yue Pan ◽

Bo Peng ◽

Haoran Liu ◽

...

Keyword(s):

Deep Learning ◽

Automated Inspection ◽

Box Girder ◽

Inspection Method ◽

Long Span Bridges ◽

Long Span ◽

Steel Box Girder

Download Full-text

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Sensors ◽

10.3390/s20061698 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1698 ◽

Cited By ~ 10

Author(s):

Jia Yin ◽

Koppaka Ganesh Sai Apuroop ◽

Yokhesh Krishnasamy Tamilselvam ◽

Rajesh Elara Mohan ◽

Balakrishnan Ramalingam ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Experimental Results ◽

Detection Accuracy ◽

Inspection Method ◽

Learning Technique ◽

Standard Quality ◽

Typical Food ◽

Human Support ◽

Inspection Task

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96 % detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.

Download Full-text

5G Edge Cloud Power Real-time Inspection Technology Based on YOLOV4-Tiny

Distributed Generation & Alternative Energy Journal ◽

10.13052/dgaej2156-3306.3643 ◽

2021 ◽

Author(s):

Jiaqi Song ◽

Jing Li ◽

Di Wu ◽

Guangye Li ◽

Jiaxin Zhang ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

High Efficiency ◽

Real Data ◽

Detection Algorithm ◽

Power Line ◽

Edge Computing ◽

Inspection Method ◽

Computing Architecture ◽

Low Efficiency

Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.

Download Full-text

Optimization Inspection Method for Concrete Girder Bridges using Vision-Based Deep Learning and Images acquired by Unmanned Aerial Vehicles

IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures ◽

10.2749/seoul.2020.257 ◽

2020 ◽

Author(s):

Shichao Wang ◽

Kaiyu Liu

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicles ◽

Inspection Method ◽

Girder Bridges ◽

Aerial Vehicles ◽

Concrete Girder

Download Full-text

Study of filming condition for deep learning based crack detection method

10.5592/co/cetra.2020.1059 ◽

2021 ◽

Author(s):

Tomotaka Fukuoka ◽

Takahiro Minami ◽

Makoto Fujiu ◽

Junichi Takayama

Keyword(s):

Image Processing ◽

Deep Learning ◽

Service Life ◽

Visual Inspection ◽

Crack Detection ◽

Detection Method ◽

Processing Method ◽

Inspection Method ◽

Image Processing Method

Recently, the study of extending the service life of bridges has gained attention. In Japan, there are about 730,000 bridges with a length of 2 m or longer, and many of these were built during a period of high economic growth, and have now reached the end of their service life. Therefore, their rebuilding and the extension of their service life must be considered. However, some local public organizations have problems that insufficient manpower relative to the number of bridges to manage, as well as insufficient funding for maintenance. Thus, these organizations are unable to perform routine close visual inspections. Specific problems include “notably less staff and consulting technicians relative to the number of bridges to be managed” and “high inspection cost preventing from funding for repair.” As issues with the continuing close visual inspection of bridges are surfacing, the remote imaging system is expected to become a new inspection method that replaces close visual inspection. The practical potential of bridge inspections using images captured with a super-high-resolution camera was examined. A super-high-resolution camera enables us to take a wide area picture of a target bridge from a long distance. An image processing method could improve the efficiency of image-based inspection method. For example, a deep learning-based image processing method could extract a damaged area on a surface of a bridge automatically with high accuracy faster than human inspection. In general, the accuracy of an image processing method is affected by the quality of an input image. Filming conditions are one of the factors that determine the quality of a photo image. It is important to evaluate the effect of filming conditions to improve the reliability of an image processing method. In this paper, we evaluate the effect of the filming conditions for an image processing method by comparing the results of a deep learning-based crack detection method.

Download Full-text

An insulator inspection method based on deep learning applicable to multi‐scale and occlusion conditions

The Journal of Engineering ◽

10.1049/tje2.12029 ◽

2021 ◽

Vol 2021 (4) ◽

pp. 216-225

Author(s):

Junpeng Wu ◽

Shaobo Tang ◽

Xianglei Li

Keyword(s):

Deep Learning ◽

Inspection Method ◽

Multi Scale

Download Full-text

Quality inspection method of micro-nano parts based on deep learning

Modern Physics Letters B ◽

10.1142/s0217984918503633 ◽

2018 ◽

Vol 32 (30) ◽

pp. 1850363

Author(s):

Dongjie Li ◽

Cong Liu

Keyword(s):

Deep Learning ◽

Detection System ◽

Detection Methods ◽

Work Intensity ◽

Inspection Method ◽

Image Information ◽

Manual Methods ◽

Low Efficiency ◽

High Work

There are some disadvantages such as low efficiency, high work intensity by using the manual methods to detect the quality of micro-nano parts because of the characteristics such as small size and fragile structure. Considering about the disadvantages, computer microscopic vision is introduced into the detection system in this paper, which can collects the image information of the parts into the computer system efficiently. The parts to be detected are transmitted by the spin material platform driven by the stepping motor. It is CNN based on deep learning that used to detect the surface quality and classify the defects of the parts according to the image information in this paper, which can improve the accuracy of the detection and reduce the work intensity of human compared with not only the traditional manual detection methods but also some edge detection methods that former researchers used.

Download Full-text

A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

Sensors ◽

10.3390/s20040980 ◽

2020 ◽

Vol 20 (4) ◽

pp. 980 ◽

Cited By ~ 3

Author(s):

Liming Zhao ◽

Fangfang Li ◽

Yi Zhang ◽

Xiaodong Xu ◽

Hong Xiao ◽

...

Keyword(s):

Deep Learning ◽

Surface Region ◽

Inspection System ◽

Scanning System ◽

Inspection Method ◽

Scanning Method ◽

Image Scanning ◽

Defects Inspection ◽

Inspection Strategy ◽

Defects Classification

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

Download Full-text