Studying the Influence of Illumination Design in Quality Inspection on Vaccine Vials Production Line Using Computer Vision

In the industrial context, there are key factors that directly affect the system’s efficiency. Higher demands for both quantity and quality in today’s market call for constant research and development of technologies for automating production and quality control. Machine vision is a solution to increase speed and accuracy in defect detection. However, applications from machine vision are only effective if there is good data input. This is the reason why a machine vision system, needs high-quality input images from a well-designed illumination system. These illumination systems are designed to highlight faults in products. Therefore, the images obtained will provide optimized data for easier image processing thus directly increase the processing speed, accuracy, and overall system performance. To achieve this goal, this paper presents a few approaches to enhance and optimize images by implements illumination techniques into a miniature model of pharmaceutical bottle assembly line using machine vision as the inspector block. In this paper, we will evaluate the critical needs of using customize illumination system for quality inspection on an assembly line.

Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot

In this study, we propose a smart hopper system that automatically unblocks obstructions caused by rocks dropped into hoppers at mining sites. The proposed system captures RGB (red green blue) and D (depth) images of the upper surfaces of hopper models using an RGB-D camera and transmits them to a computer. Then, a virtual hopper system is used to identify rocks via machine vision-based image processing techniques, and an appropriate motion is simulated in a robot arm. Based on the simulation, the robot arm moves to the location of the rock in the real world and removes it from the actual hopper. The recognition accuracy of the proposed model is evaluated in terms of the quantity and location of rocks. The results confirm that rocks are accurately recognized at all positions in the hopper by the proposed system.

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

A close-loop verification approach for pedestrian stability based on machine vision

In public places, it is significant to analyze the stability of the crowd which can support the crowd management and control, and protect the evacuees safely and effectively. The numerical analysis method of system stability based on Lyapunov theory suffers problems that it is difficult to avoid random errors in the initialization of pedestrian density and velocity, as well as cumulative errors due to time increasing, limiting its application. This study adopts a complementary model of theoretical numerical analysis and machine vision with a parallel convolutional neural network (CNN) model. It proposes an approach of stability analysis and closed-loop verification for crowd merging systems. Thereby, this research provides theoretical and methodological support for planning of the functional layout of crowd flow in public crowd-gathering places and the control measures for stable crowd flow.