A family of experiments to investigate the effects of groupware for software inspection

It is widely accepted that the inspection of software artifacts can find defects early in the development process and gather information on the quality of the evolving product. However, the inspection process is resource-intensive and involves tedious tasks, such as searching, sorting, and checking. Tool support for inspections can help accelerating these tasks and allows inspectors to concentrate on tasks particularly needing human attention. Only few tools are available for inspections. We have thus developed a set of groupware tools for both individual defect detection and inspection meetings to lower the effort of inspections and to increase their efficiency. This paper presents the Groupware-supported Inspection Process (GrIP) and describes tools for inspecting software requirements. As only little empirical work exists that directly compares paper-based and tool-based software inspection, we conducted a family of experiments in an academic environment to empirically investigate the effect of tool support regarding defect detection and inspection meetings. The main results of our family of experiments regarding individual defect detection are promising: The effectiveness of inspectors and teams is comparable to paper-based inspection without tool support; the inspection effort and defect overlap decreases significantly with tool support, while the efficiency of inspection teams increases considerably. Regarding tool support for inspection meetings the main findings of the experiments are that tool support considerably lowers the meeting effort, supports inspectors in identifying false positives, and reduces the number of true defects lost during a meeting. The number of unidentified false positives is still quite high.     

Automated visual inspection in the semiconductor industry: A survey

Automated visual inspection is an image-processing technique for quality control and production line automation. This paper reviews various optical inspection approaches in the semiconductor industry and categorize the previous literatures by the inspection algorithm and inspected products. The vision-based algorithms that had been adopted in the visual inspection systems include projection methods, filtering-based approaches, learning-based approaches, and hybrid methods. To discuss about the practical applications, the semiconductor industry covers the manufacturing and production of wafer, thin-film transistor liquid crystal displays, and light-emitting diodes. To improve the yield rate and reduce manufacturing costs, the inspection devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. To achieve a high robustness and computational efficiency of automated visual inspection, interdisciplinary knowledge between precision manufacturing and advanced image-processing techniques is required in the novel system design. This paper reviews multiple defect types of various inspected products which can be referenced for further implementations and improvements.     

Optimized allocation of defect inspection capacity with a dynamic sampling strategy

In semiconductor manufacturing, in-line inspections are necessary to monitor processes, products and tools in order to reduce excursions and achieve high yields of final products. However, capacity is limited and inspections directly impact the cycle times of products. Sampling strategies are used to improve product yields while limiting the number of inspections, and thus the impact on the cycle times of the inspected lots. Dynamic sampling has been recently introduced and new models are required to estimate the associated inspection capacity. In this paper, we focus on micro-defect inspections and the risk on process tools in terms of Wafers at Risk (W@R), which is the number of wafers processed on a tool since its latest defect inspection. A linear programming model that estimates the required defect inspection capacity to satisfy the W@R limits on process tools is proposed. Our model can be used at different decision levels. At the tactical level, it shows if W@R limits can be satisfied when the product mix changes and/or if planned W@R reductions can be met with the available inspection capacity. At the strategic level, the model helps to justify capacity investments if the objectives in terms of W@R reduction cannot be achieved with the available inspection capacity. Numerical experiments on industrial data are performed and discussed.     

Optimal path planning for automated dimensional inspection of free-form surfaces

Structural dimensional inspection is vital for the process monitoring, quality control, and fault diagnosis in the mass production of auto bodies. Comparing with the non-contact measurement, the high-precision five-axis measuring machine with the touch-trigger probe is a preferred choice for data collection. It can assist manufacturers in making accurate inspection quickly. As the increase of free-form surfaces and diverse surface orientations in product design, existing inspection approaches cannot capture some new critical features in the curvature of products in an efficient way. Therefore, we need to develop new path planning methods for automated dimensional inspection of free-form surfaces. This paper proposes an optimal path planning system for automated programming of measuring point inspection by incorporating probe rotations and effective collision detection. Specifically, the methodological contributions include: (i) a dynamic searching volume-based algorithm is developed to detect potential collisions in the local path between measurement points; (ii) a local path generation method is proposed with the integration of the probe trajectory and the stylus rotation. Then, the inspection time matrix is proposed to quantify the measuring time of diverse local paths; (iii) an optimization approach of the global inspection path for all critical points on the product is developed to minimize the total inspection time. A case study has been conducted on an auto body to verify the performance of the proposed method. Results show that the collision-free path for the free-form auto body could be generated automatically with off-line programming, and the proposed method produces about 40% fewer dummy points and needs 32% less movement time in the auto body inspection process.     

Automatic visual inspection: An approach with multi-instance learning

One of the industrial applications of computer vision is automatic visual inspection. In the last decade, standard supervised learning methods have been used to detect defects in different kind of products. These methods are trained with a set of images where every image has to be manually segmented and labeled by experts in the application domain. These manual segmentations require a large amount of high quality delineations (on pixels), which can be time consuming and often a difficult task. Multi-instance learning (MIL), in contrast to standard supervised classifiers, avoids this task and can, therefore, be trained with weakly labeled images. In this paper, we propose an approach for the automatic visual inspection that uses MIL for defect detection. The approach has been tested with data from three artificial benchmark datasets and three real-world industrial scenarios: inspection of artificial teeth, weld defect detection and fishbone detection. Results show that the proposed approach can be used with weakly labeled images for defect detection on automatic visual inspection systems. This approach is able to increase the area under the receiver-operating characteristic curve (AUC) up to 6.3% compared with the naïve MIL approach of propagating the bag labels.     

Automated visual inspection of rolled metal surfaces

A prototype for an automated visual on-line metal strip inspection system is described. The system is capable of both detecting and classifying surface defects in copper alloy strips, and it has been installed for evaluation in a production line in a rolling mill. The image acquisition part of the system is based on a CCD line scan camera and condensing bright field illuminators. The inspection algorithms are based on morphological preprocessing and combined statistical and structural defect recognition. The image processing hardware consists of commercial modules. An analysis of this implementation is presented. A similar inspection principle has also been successfully applied to steel strip inspection.     

ASSIST—a tool to support software inspection

Software inspection is a valuable technique for defect detection. Recent research has considered the development of computer support, with the aim of providing even greater benefits when applying inspection. This has resulted in the development of a number of prototype support systems. These suffer from some fundamental limitations, however. Asynchronous/Synchronous Software Inspection Support Tool (ASSIST) is designed to tackle these limitations and to provide a platform for rigorous investigation of the inspection process. It uses a custom-designed language known as Inspection Process Definition Language to allow support of any inspection process, and provides an open, expandable system allowing multiple document types to be catered for easily. A number of facilities designed to enhance the inspection process are also present.     

Deep-learning-based anomaly detection for lace defect inspection employing videos in production line

Defect inspection plays an essential role in ensuring quality of industrial products. The most widely used human visual inspection method has some drawbacks such as high cost and low efficiency, which bring an eager demand for the application of automatic defect inspection algorithm in actual production. However, few industrial production lines use automatic detection devices due to the gap between data collected in the actual production environment and ready-made datasets. Lace is one of the industrial products which completely depends on manual defect inspection. The complex and fine texture of lace makes it difficult to extract regular patterns using the existing image-based defect inspection methods. In this paper, we propose to collect lace videos in the weaving stage and design a deep-learning-based anomaly detection framework to detect lace defects. The framework contains three stages, namely video pre-processing stage, pixel reconstruction stage and pixel classification stage. In the offline phase, only defect-free lace videos are needed to train the pixel reconstruction model and calculate the detection threshold by our adaptive thresholding method. In the online phase, the proposed framework reconstructs lace videos and performs defect inspection using reconstruction error and the pre-set threshold. As far as we know, this paper the first to detect fabric defects by videos. Experimental results on artificial defect videos demonstrate the effectiveness of the proposed framework.     

微型钻头端刃侧刃缺陷的全景式视觉检测技术

在微钻生产中采用机器视觉方法进行缺陷检测时,其难点在于一次拍摄就获得微钻侧刃的完整的、高分辨率的图像.当采用高倍率光学镜头时,又产生视野与检测范围的矛盾.为解决该矛盾,本文创造性地设计了一套内锥镜面反射成像装置,既可获得微钻端刃的清晰图像,又可获得整个微钻侧刃的清晰图像,使得微钻缺陷的全自动视觉检测成为可能.试验结果表...     

Recent advances in the automatic inspection of integrated circuits for pattern defects

Automatic inspection has become an essential part of manufacturing technology for integrated circuit (IC) chips, but three trends in the geometries of ICs and the chips that they comprise have serious implications for inspection, making further advances in technology challenging. The individual devices (e.g., transistors) are becoming smaller, with the smallest features on some advanced products already crossing the optical resolution threshold; the chip areas are becoming larger; and the chips consist of more layers and undergo more processing steps. Not only are the smallest defects more difficult to detect due to the optical resolution limit, they are also much rarer because the tolerable defect density decreases as the chip area increases. This paper addresses automated IC inspection, surveying recent advances and future challenges. An overview of all inspection on IC chips during the manufacturing process is followed by a detailed discussion of pattern defect inspection (PDI) and its unique requirements, such as detection probability, false alarm rate, throughput, and minimum defect size. The core material of the paper consists of a discussion of approaches and systems for PDI, emphasizing recent developments, but reviewing older work to set the proper context. Both work reported in the literature and commercial systems are considered.     

基于MapReduce的液晶屏缺陷检测方法

5th液晶屏在生产过程中会产生多种类型的缺陷,通过单一节点进行缺陷检测存在存储资源和计算时间的瓶颈。利用Hadoop集群的分布式计算、存储能力处理海量的高分辨率液晶屏图像是一个新的思路。针对高分辨液晶屏图像缺陷局部性特点,设计基于MapReduce的分布式缺陷检测方法,对高分辨率图像分块,并行完成每块图像的缺陷检测,再将检测结果归并,从而解决高分辨率图像缺陷检测效率低下问题。通过运行在Hadoop平台上的实验表明,该方法在完成缺陷检测的同时具有良好的效率提升。     

Automating the software inspection process

Inspection is widely believed to be the most cost-effective method for detecting defects in documents produced during the software development lifecycle. However, it is by its very nature a labour intensive process. This has led to work on computer support for the process which should increase the efficiency and effectiveness beyond what is currently possible with a solely manual process. In this paper, we first of all describe current approaches to automation of the inspection process. There are four main areas of inspection which have been the target for computer support: document handling, individual preparation, meeting support and metrics collection. We then describe five tools which have been developed to support the inspection process and compare the capabilities of these tools. This is followed by a fuller discussion of the features which could be provided by computer support for inspection and the gains that may be achieved by using such support.     

机器视觉在轨道交通系统状态检测中的应用综述

城市轨道交通系统主要由弓/网系统、轨道线路、车辆、车站等组成,传统的人工巡检等方法检测效率低、劳动强度大、自动化和智能化程度不高,给城市轨道交通的运营保障和进一步健康发展带来了巨大的挑战.机器视觉作为一种重要的检测手段,在城市轨道交通系统状态检测领域得到了广泛的应用.鉴于此,针对机器视觉在城市轨道交通系统安全状态检测中...     

竹地板选片工艺的机器视觉化研究及实现

针对竹地板加工中的“选片工艺”,构建了基于机器视觉的竹片缺陷检测与颜色分拣平台,研究了竹片缺陷与颜色检测过程中图像采集、光学成像、光学照明等关键问题,设计了竹片缺陷检测及颜色识别的图像处理算法及软件流程,并探讨了竹片缺陷检测与颜色分拣平台的机械传动、分拣执行装置及电控实现。仿真实验表明,提出的竹片缺陷检测及颜色识别算法能够对采集系统摄取的竹片图像进行准确的检测与识别,能够完成竹片6种常见的缺陷检测以及4种以上的色差识别,对提高选片工艺的生产效率具有重要意义。     

Wafer defect inspection by neural analysis of region features

Wafer defect inspection is an important process that is performed before die packaging. Conventional wafer inspections are usually performed using human visual judgment. A large number of people visually inspect wafers and hand-mark the defective regions. This requires considerable personnel resources and misjudgment may be introduced due to human fatigue. In order to overcome these shortcomings, this study develops an automatic inspection system that can recognize defective LED dies. An artificial neural network is adopted in the inspection. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in the experiments. The results show that the proposed approach successfully identified the defective dies on LED wafers. Personnel costs and misjudgment due to human fatigue can be reduced using the proposed approach.     

Automatic defect detection of metro tunnel surfaces using a vision-based inspection system

Due to the impact of the surrounding environment changes, train-induced vibration, and human interference, damage to metro tunnel surfaces frequently occurs. Therefore, accidents caused by the tunnel surface damage may happen at any time, since the lack of adequate and efficient maintenance. To our knowledge, effective maintenance heavily depends on the all-round and accurate defect inspection, which is a challenging task, due to the harsh environment (e.g., insufficient illumination, the limited time window for inspection, etc.). To address these problems, we design an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, which covers the design of hardware and software parts. For the hardware component, we devise a data collection system to capture tunnel surface images with high resolution at high speed. For the software part, we present a tunnel surface image pre-processing approach and a defect detection method to recognize defects with high accuracy. The image pre-processing approach includes image contrast enhancement and image stitching in a coarse-to-fine manner, which are employed to improve the quality of raw images and to avoid repeating detection for overlapped regions of the captured tunnel images respectively. To achieve automatic tunnel surface defect detection with high precision, we propose a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN). Our image pre-processing and the defect detection methods also promising performance in terms of recall and precision, which is demonstrated through a series of practical experimental results. Moreover, our MTSIS has been successfully applied on several metro lines.     

基于二值投影的PCB元件安装缺陷检测算法研究

研究分析了适用于AOI设备的PCB表面安装元件的缺陷检测算法.使用二值投影分析方法对2种元件类型的缺陷检测方法进行了研究,包括针对贴片电阻电容类型的chip元件和集成电路芯片类型的IC元件的缺陷检测方法.使用VC++6.0编写MFC程序实现算法,并制作了各种元件图像进行实验测试.实验结果表明,提出的方法能够快速有效的对两种类型的元件安装缺陷进行检测.