• Journal of IKEEE
• /
• v.28 no.3
• /
• pp.474-478
• /
• 2024

In this paper, we proposes a deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers. The proposed deep learning network is specifically designed for pharmaceutical containers by using data produced in real manufacturing environments, leading to more accurate quality inspection. Additionally, the use of an inline-capable deep learning network allows for an increase in inspection speed. The development of the deep learning network for quality inspection in the multi-camera inline inspection system consists of three steps. First, a dataset of approximately 10,000 images is constructed from the production site using one line camera for foreign substance inspection and three area cameras for dimensional inspection. Second, the pharmaceutical container data is preprocessed by designating regions of interest (ROI) in areas where defects are likely to occur, tailored for foreign substance and dimensional inspections. Third, the preprocessed data is used to train the deep learning network. The network improves inference speed by reducing the number of channels and eliminating the use of linear layers, while accuracy is enhanced by applying PReLU and residual learning. This results in the creation of four deep learning modules tailored to the dataset built from the four cameras. The performance of the proposed deep learning network for quality inspection in the multi-camera inline inspection system for pharmaceutical containers was evaluated through experiments conducted by a certified testing agency. The results show that the deep learning modules achieved a classification accuracy of 99.4%, exceeding the world-class level of 95%, and an average classification speed of 0.947 seconds, which is superior to the world-class level of 1 second. Therefore, the effectiveness of the proposed deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers has been demonstrated.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.12
• /
• pp.7-13
• /
• 1998

In this paper, an automatic ball-stud inspection system has been developed using the computer-aided vision system. Index table has been used to get the rapid measurement and multi-camera has been used to get the high resolution in physical system. Camera calibration was suggested to perform the reliable inspection. Image processing and data analysis algorithms for ball stud inspection system have been investigated and were performed quickly with high accuracy. As a result, inspection system of a ball stud could be used with a high resolution in real time.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.631-632
• /
• 2006

The assembly of camera lens modules fur the mobile phone has not been automated so far. They are still assembled manually because of high precision of all parts and hard-to-recognize lens by vision camera. In addition, the very short life cycle of the camera phone lens requires flexible and intelligent automation. This study proposes a fast and accurate identification system of the parts by distributing the camera for 4 degree of freedom assembly robot system. Single or multi-cameras can be installed according to the part's image capture and processing mode. It has an agile structure which enables adaptation with the minimal job change. The framework is proposed and the experimental result is shown to prove the effectiveness.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.1120-1123
• /
• 1995

In this paper, an automatic B/STUD inspection system has been developed using the computer aided vision system. Index Table has been used to get the rapid measurement and multi-camera has been used to get the high resolution in mechanical system. Camera calibration was suggested to perform the reliable Inspection. Image processing and data analysis algorithms for B/STUD inspection system has been investigated and were performed quickly with high accuracy. As a result, Inspection system of a B/STUD can be measured with a high resolution in real time.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.1
• /
• pp.453-458
• /
• 2016

One of the most common applications of machine vision is quality inspections in automated production. In this study, a welding inspection system that is controlled by a PC and a PLC equipped with a multi-camera setup was developed. The system was designed to measure the primary dimensions, such as the length and width of the welding areas. The TCP/IP protocols and multi-threading techniques were used for parallel control of the optical components and physical distribution. A coaxial light was used to maintain uniform lighting conditions and enhance the image quality of the weld areas. The core image processing system was established through a combination of various algorithms from the OpenCV library. The proposed vision inspection system was fully validated for an actual weld production line and was shown to satisfy the functional and performance requirements.

• Journal of IKEEE
• /
• v.28 no.3
• /
• pp.469-473
• /
• 2024

In this paper proposes a study on the development of a multi-camera inline inspection system using machine vision for quality inspection of pharmaceutical containers. The proposed technique captures the pharmaceutical containers from multiple angles using several cameras, allowing for more accurate quality assessment. Based on the captured data, the system inspects the dimensions and defects of the containers and, upon detecting defects, notifies the user and automatically removes the defective containers, thereby enhancing inspection efficiency. The development of the multi-camera inline inspection system using machine vision is divided into four stages. First, the design and production of a control unit that fixes or rotates the containers via suction. Second, the design and production of the main system body that moves, captures, and ejects defective products. Third, the design and development of control logic for the embedded board that controls the entire system. Finally, the design and development of a user interface (GUI) that detects defects in the pharmaceutical containers using image processing of the captured images. The system's performance was evaluated through experiments conducted by a certified testing agency. The results showed that the dimensional measurement error range of the pharmaceutical containers was between -0.30 to 0.28 mm (outer diameter) and -0.11 to 0.57 mm (overall length), which is superior to the global standard of 1 mm. The system's operational stability was measured at 100%, demonstrating its reliability. Therefore, the efficacy of the proposed multi-camera inline inspection system using machine vision for the quality inspection of pharmaceutical containers has been validated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.4160-4173
• /
• 2019

Borescopes facilitate the inspection of areas inside machines and systems that are not directly accessible for visual inspection. They offer real-time, up-close access to confined and hard-to-access spaces without having to dismantle or destructure the object under inspection. Borescopes are ideal instruments for routine maintenance, quality inspection and monitoring of systems and structures. The main application being fault or defect detection, it is useful to have measuring capability to quantify object dimensions in a target area. High-end borescopes use multi-optic solutions to provide measurement information of viewed objects. Multi-optic solutions can provide accurate measurements at the expense of structural complexity and cost increase. Measuring functionality is often unavailable in low-end, single camera borescopes. In this paper, a single camera measurement solution that enables the size estimation of viewed objects is proposed. The proposed solution computes and overlays a scaled grid of known spacing value over the screen view, enabling the human inspector to estimate the size of the objects in view. The proposed method provides a simple means of measurement that is applicable to low-end borescopes with no built-in measurement capability.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.9
• /
• pp.1406-1410
• /
• 2015

The railway catenary systems consist of a complex structure, electrical and mechanical conditions are exposed to dangerous environment due to current collection between pantograph and catenary systems. These two types of conditions for the catenary systems are critically assessed for management and are becoming increasingly more dependent on reliable inspection system. In this paper, a new measurement system which enables inspection of catenary faults was studied and implemented. The system uses wavelength band of ultraviolet (UV) and IR (Infrared) to measure the insulation of corona and temperature of catenary wire. In order to determine the sensitivity of implemented system, the performance test such as distance detection range of UV camera and temperature resolution of IR camera were conducted, respectively. The field test of development system was also conducted in metro and high-speed line using mobile type UV and IR multi-composited measurement systems.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2275-2278
• /
• 2003

In this paper, we propose a visual inspection algorithm to detect can print-errors by using multi-camera and image valuing algorithm. The features of the algorithm are to use four cameras that are arranged with 90$^{\circ}$ between each other and to adopt a synthesized image model which represents whole surface of a can. Using the model, detection process is straight forward, namely it is comparing a partial region of the can to a specific region of the model where is previously marked.

• Nuclear Engineering and Technology
• /
• v.46 no.3
• /
• pp.439-446
• /
• 2014

An amphibious inspection robot system (hereafter AIROS) is being developed to visually inspect the in-containment refueling storage water tank (hereafter IRWST) strainer in APR1400 instead of a human diver. Four IRWST strainers are located in the IRWST, which is filled with boric acid water. Each strainer has 108 sub-assembly strainer fin modules that should be inspected with the VT-3 method according to Reg. guide 1.82 and the operation manual. AIROS has 6 thrusters for submarine voyage and 4 legs for walking on the top of the strainer. An inverse kinematic algorithm was implemented in the robot controller for exact walking on the top of the IRWST strainer. The IRWST strainer has several top cross braces that are extruded on the top of the strainer, which can be obstacles of walking on the strainer, to maintain the frame of the strainer. Therefore, a robot leg should arrive at the position beside the top cross brace. For this reason, we used an image processing technique to find the top cross brace in the sole camera image. The sole camera image is processed to find the existence of the top cross brace using the cross edge detection algorithm in real time. A 5-DOF robot arm that has multiple camera modules for simultaneous inspection of both sides can penetrate narrow gaps. For intuitive presentation of inspection results and for management of inspection data, inspection images are stored in the control PC with camera angles and positions to synthesize and merge the images. The synthesized images are then mapped in a 3D CAD model of the IRWST strainer with the location information. An IRWST strainer mock-up was fabricated to teach the robot arm scanning and gaiting. It is important to arrive at the designated position for inserting the robot arm into all of the gaps. Exact position control without anchor under the water is not easy. Therefore, we designed the multi leg robot for the role of anchoring and positioning. Quadruped robot design of installing sole cameras was a new approach for the exact and stable position control on the IRWST strainer, unlike a traditional robot for underwater facility inspection. The developed robot will be practically used to enhance the efficiency and reliability of the inspection of nuclear power plant components.