Abstract
We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object’s location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments.
Highlights
IntroductionThe smart factory describes a future state of fully connected and functioning manufacturing systems conducting all the required tasks, from customer orders to production, without the need for human involvement [1]
The smart factory describes a future state of fully connected and functioning manufacturing systems conducting all the required tasks, from customer orders to production, without the need for human involvement [1].Recently, due to the COVID-19 pandemic, factories have accelerated the transition to contactless services in the manufacturing industry and smart factory transformation by utilizing advanced ICT technologies such as AI, Internet of Things (IoT), cloud, robots, and big data
Smart factories are evolving to become intelligent by combining major ICT technologies such as IoT, robots, and AI to connect all processes of production, distribution, service, and consumption
Summary
The smart factory describes a future state of fully connected and functioning manufacturing systems conducting all the required tasks, from customer orders to production, without the need for human involvement [1]. In manufacturing factories with high variability, it is difficult to accurately detect object localization and information due to various environmental factors, such as noise around objects, light reflections, and illumination interference To solve this problem, we propose a robust algorithm for object localization that can and quickly respond to changes in the external environment by improving a previous work [7]. Background subtractions are sensitive to external environment changes (brightness of light, reflection, shadow, etc.) and noise, making it difficult to detect objects [10] Representative methods of this algorithm include the mean filter, median filter method, adaptive thresholding, and Gaussian mixture methods that make the average of the previous image the background of the current image [11]. There are various background subtraction techniques, such as optical flow and shadow detection [15,16,17,18,19,20]
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