Integrated process of images and acceleration measurements for damage detection

The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd

High Speed Vision Based Automatic Inspection and Path Planning for Processing Conveyed Objects

AbstractUnder the pressure of cost reduction and productivity improvement, this paper presents a new methodology which provides a fast inspection of defective objects and generates a real time motion trajectory for processing objects being conveyed with high speed in an industrial large-scale production. The image data obtained by a multispectral imaging system is analyzed within image processing algorithms using classification methods based on support vector machine. These data provide a basis for a path planning algorithm which considers location, orientation and arrangement of defects on the conveyed objects. Selective processing tool guided by the planed path is motion controlled

A THREE DIMENSIONAL (3D) VISION BASED DEFECT INSPECTION SYSTEM FOR GLUING APPLICATION

A Robot Vision System (RVS) is an adaptive and dynamic system that caters to a wide range of jobs where each involves a set of operations required to be done at a predetermined workstation. This research is focused on the development of a vision system to be integrated with KUKA arm robot. Pyramid object is used as a complimentary of the windscreen car as a model. It developed using plain cardboard with dimension of 15cm x 15cm. 2D matching application introduced to identify the characteristic of the object used in the system using CCD camera. Object used must be trained in training phase to create object template and used again in recognition phase for object classification. Then, two CCD cameras are used; placed at the top and front of the object to extract object’s edge location using Harris Point. Data extracted from it are used to find 3D coordination of each edge. Equation of straight line mostly used in this method to identify x, y and z coordinates. Data obtained from the system then used to give instruction to KUKA arm robot for gluing purposes. Pixel coordinates must be converted to robot coordinates for easier understanding by the robot. Three types of defect are trained as model templates and save to the memory known as bumper, gap and bubble defect. Each defect has special characteristic. Inspection system developed to identify problems occurs in gluing process. Template matching method used to call model trained in training phase to identify the uncertainties. Each defect occurs comes with its coordinate’s information for correction. Correction of defect consists of two phase; 1st CoD where correction is completed in first time and 2nd CoD where correction still need to be completed after the first correction. Data for all the process are recorded to prove that this algorithm made improvement with the previous research

Robotic Defect Inspection with Visual and Tactile Perception for Large-scale Components

In manufacturing processes, surface inspection is a key requirement for quality assessment and damage localization. Due to this, automated surface anomaly detection has become a promising area of research in various industrial inspection systems. A particular challenge in industries with large-scale components, like aircraft and heavy machinery, is inspecting large parts with very small defect dimensions. Moreover, these parts can be of curved shapes. To address this challenge, we present a 2-stage multi-modal inspection pipeline with visual and tactile sensing. Our approach combines the best of both visual and tactile sensing by identifying and localizing defects using a global view (vision) and using the localized area for tactile scanning for identifying remaining defects. To benchmark our approach, we propose a novel real-world dataset with multiple metallic defect types per image, collected in the production environments on real aerospace manufacturing parts, as well as online robot experiments in two environments. Our approach is able to identify 85% defects using Stage I and identify 100% defects after Stage II. The dataset is publicly available at https://zenodo.org/record/8327713Comment: This is a pre-print for International Conference on Intelligent Robots and Systems 2023 publicatio

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results

Scanning from heating: 3D shape estimation of transparent objects from local surface heating

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented

Automatic Color Inspection for Colored Wires in Electric Cables

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

Bridge Inspection: Human Performance, Unmanned Aerial Systems and Automation

Unmanned aerial systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS-denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving image-processing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification