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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 36, Number 4(2) (2024)
Copyright(C) MYU K.K.
pp. 1389-1404
S&M3606 Research Paper of Special Issue
https://doi.org/10.18494/SAM4840
Published: April 19 , 2024

Hybrid Deep Learning and FAST–BRISK 3D Object Detection Technique for Bin-picking Application [PDF]

Thanakrit Taweesoontorn, Sarucha Yanyong, and Poom Konghuayrob

(Received January 9, 2024; Accepted February 28, 2024)

Keywords: computer vision, object detection and pose estimation, bin-picking application, robot operating system

In the field of industrial robotics, robotic arms have been significantly integrated, driven by their precise functionality and operational efficiency. We here propose a hybrid method for bin-picking tasks using a collaborative robot, or cobot combining the You Only Look Once version 5 (YOLOv5) convolutional neural network (CNN) model for object detection and pose estimation with traditional feature detection based on the features from accelerated segment test (FAST) technique, feature description using binary robust invariant scalable keypoints (BRISK) algorithms, and matching algorithms. By integrating these algorithms and utilizing a low-cost depth sensor camera for capturing depth and RGB images, the system enhances real-time object detection and pose estimation speed, facilitating accurate object manipulation by the robotic arm. Furthermore, the proposed method is implemented within the robot operating system (ROS) framework to provide a seamless platform for robotic control and integration. We compared our results with those of other methodologies, highlighting the superior object detection accuracy and processing speed of our hybrid approach. This integration of robotic arm, camera, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Corresponding author: Poom Konghuayrob


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Thanakrit Taweesoontorn, Sarucha Yanyong, and Poom Konghuayrob, Hybrid Deep Learning and FAST–BRISK 3D Object Detection Technique for Bin-picking Application, Sens. Mater., Vol. 36, No. 4, 2024, p. 1389-1404.



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