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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 International Carnahan Conference on Security Technology (ICCST) - Montreal, QC (2018.10.22-2018.10.25)] 2018 International Carnahan Conference on Security Technology (ICCST) - X-ray Screening of Hold Baggage: Are the Same Visual-Cognitive Abilities Needed for 2D and 3D Imaging?
摘要： 2D multi-view X-ray imaging technology is widely used for security screening of hold baggage at airports. Newer technology is based on 3D CT imaging. Such systems offer the possibility to rotate a bag around 360 degrees. With the transition from 2D multi-view to advanced CT imaging, the question arises whether airport security officers (screeners) need the same visual-cognitive abilities when visually inspecting X-ray images of hold baggage. This study investigated the relationship between visual-cognitive abilities and visual inspection performance of screeners. Screeners conducted a computer-based visual cognitive test battery (VCTB) and a simulated hold baggage screening task with 2D and 3D imaging. We found that aspects of processing speed and visual processing correlated significantly with visual inspection performance of screeners using 2D imaging technology. In comparison, performance of screeners that visually inspected 3D images showed less correlations with the VCTB. These results indicate that with the expected change from 2D to 3D imaging technology in airport security, visual-cognitive requirements of the screeners might change. Therefore, further studies need to elucidate in more detail what visual-cognitive skills future 3D screeners need as it could affect personnel selection and development.
关键词： visual inspection,X-ray imaging technology,airport security,2D multi-view imaging,visual cognitive abilities,operator performance,3D imaging,hold baggage screening
更新于2025-09-19 17:15:36
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Photovoltaic Module Reliability || Failure Analysis Tools
摘要： Regardless of whether a module has degraded from field exposure or accelerated stress testing, it is important to understand what has actually changed within the module that led to lost peak power. If we want to use the results to improve the module construction so that future modules will not degrade, we must understand what particular changes have occurred. In this chapter, we will explore some of the methods used to better understand what has gone wrong within the module. Methods reviewed include, analysis of the I–V parameters, measurement of performance at different irradiances, visual inspection, Infrared (IR) Inspection, Electroluminescence (EL) and evaluation of adhesion. Each will be discussed in the subsections that follow.
关键词： Infrared Inspection,Electroluminescence,Adhesion,I–V Curve,PV Module,Failure Analysis,Visual Inspection
更新于2025-09-19 17:13:59
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Image-Alignment Based Matching for Irregular Contour Defects Detection
摘要： Automatic defect inspection is attractive for high-quality workpiece manufacturing with irregular contours in order to achieve high accuracy and no contour defect. Thus, a novel image alignment-based feature matching algorithm framework is proposed in this paper. It can be used to solve the specified pixel-level defect detection and location problems for workpieces with irregular contours. A new forensic hash is firstly generated by extracting the scale, position and main orientation information of feature points. Since the forensic hash is invariant to rotation, translation and scaling, it is used for feature matching. A feature matching method based on a robust cascade estimator is secondly proposed to establish an accurate correspondence between the test image and reference image according to the obtained image hash and a parameter space voting mechanism. Thirdly, the matched feature points are used to estimate the similar transformation parameters to achieve an accurate image alignment. Finally, image difference and morphological technique are used to locate the contour defect. Experimental results demonstrate that the proposed algorithm can effectively detect and locate small contour defects in irregular stamping workpieces.
关键词： Feature matching,Irregular contour,Visual inspection,Image alignment
更新于2025-09-10 09:29:36
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In-water visual ship hull inspection using a hover-capable underwater vehicle with stereo vision
摘要： Underwater visual inspection is an important task for checking the structural integrity and biofouling of the ship hull surface to improve the operational safety and efficiency of ships and floating vessels. This paper describes the development of an autonomous in‐water visual inspection system and its application to visual hull inspection of a full‐scale ship. The developed system includes a hardware vehicle platform and software algorithms for autonomous operation of the vehicle. The algorithms for vehicle autonomy consist of the guidance, navigation, and control algorithms for real‐time and onboard operation of the vehicle around the hull surface. The environmental perception of the developed system is mainly based on optical camera images, and various computer vision and optimization algorithms are used for vision‐based navigation and visual mapping. In particular, a stereo camera is installed on the underwater vehicle to estimate instantaneous surface normal vectors, which enables high‐precision navigation and robust visual mapping, not only on flat areas but also over moderately curved hull surface areas. The development process of the vehicle platform and the implemented algorithms are described. The results of the field experiment with a full‐scale ship in a real sea environment are presented to demonstrate the feasibility and practical performance of the developed system.
关键词： computer vision,underwater robotics,autonomous underwater vehicles,underwater navigation,visual inspection
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Weld Classification Using Gray Level Co-Occurrence Matrix and Local Binary Patterns
摘要： This paper presents an algorithm that can classify weld seams from images, exploiting machine learning techniques. Manual visual inspection is the primary way of evaluating weld seams, in cases where the primary goal is to keep inspection costs low. Such, visual inspections entail manual interpretation and evaluation, which are both time consuming and the result often depends on the person assigned to the task. These drawbacks render automatic visual inspection appealing. Thus, this paper seeks to find a possible solution for the visual inspection of welds, where two feature extraction methods are examined and tested in conjunction with two different classifiers. We investigate whether visual inspection based on texture-describing features, processed with a machine learning algorithm, can detect flaws and defects in a weld merely by inspecting the surface of the object, in a way similar to how human eyes detect them and we achieve 96% classification accuracy on a new dataset.
关键词： machine learning,visual inspection,Gray Level Co-Occurrence Matrix,weld classification,Local Binary Patterns
更新于2025-09-09 09:28:46