Real-Time Robotic Grasping and Localization Using Deep Learning-Based Object Detection Technique

Farag, Mohannad; Ghafar, Abdul; Alsibai, Mohammed Hayyan

doi:10.1109/i2cacis.2019.8825093

Cited by 12 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that this method has better performance than other methods in real datasets. Farag et al [128] proposed a real-time object detection algorithm based on a selective flexible assembly manipulator (SCARA) for robot grasping and positioning in industrial assembly lines. The motion of a SCARA robot is composed of two parts: target detection based on deep learning and position measurement based on edge detection.…”

Section: E Real-time Motionmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

show abstract

Section: E Real-time Motionmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Intelligent manufacturing equipment, when integrated with computer vision, can utilize image recognition to determine the location of the desired object and carry out tasks such as relocation and gripping [2]. The technology of vision inspection has a wide variety of applications in the industrial sector, such as the application of robots for the sorting of materials such as wood, stone, and metal [3], as well as conducting fault identification on the surface of steel materials [4] and guided robot assembly [5]. An essential technique in industrial machine vision is Deep Learning.…”

Section: Introductionmentioning

confidence: 99%

A Method for Target Detection Based on Synthetic Samples of Digital Twins

Dong,

Yang,

Wang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Machine vision technology for target detection is crucial in industrial production and manufacturing. Industries may improve productivity with accurate target detection, which requires abundant samples for training; however, it is expensive and difficult to obtain because of enterprise data privacy and security constraints. This paper proposes a method for quickly creating synthetic samples based on digital twins as a solution to the challenge. First, utilizing the virtual engine to replicate the real detecting environment, generate a range of sample photos, and extract the target object's three-dimensional coordinates in the virtual scene. Subsequently, an annotation method is designed for synthetic samples obtained from the virtual scene, utilizing principles of three-dimensional coordinate transformation and perspective coordinate transformation. This method efficiently produces numerous labeled samples with diverse annotations. Ultimately, the model performs detection tasks in the actual world using the synthetic samples as training data. The experimental results show that the synthetic samples created by this method based on digital twins can substitute real samples and effectively identify target objects during actual detection tasks. This paper proposes a unique synthetic samples strategy that decreases sample collection costs and privacy risks and solves machine vision detection technology's sample limitations-induced detection performance drop.

show abstract

“…Consequently, the robot business is seeing tremendous growth [1,2]. The most recent industrial revolution has led to an increase in the use of robotic technologies [2][3][4][5]. Artificially intelligent or remotely controlled robots must have minimal supervision when interacting with humans.…”

Section: Introductionmentioning

confidence: 99%

Indoor Automated Fire Extinguisher System using Computer Vision

Rosle,

Chia,

Nasir

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper presents the development of an automated fire extinguisher robot by employing fire recognition using computer vision. This project aims to develop a robot that can search, detect and extinguish small flames for indoor purposes. The robot is developed by applying open-source image processing to recognize the presence of fire. A control program was built to control the movement of the robot’s servo motor. The performance of the fire recognition was analysed in different light intensities and angles. The development of this project makes use of a camera and computer vision in place of various sensors, such as gas sensors, temperature sensors, and infrared sensors, to detect fires. Furthermore, a microprocessor is employed to operate the water pump and servo motor, which drive the nozzle to the position specified by the microcontroller. The results reveal that the proposed vision-based fire detection system has a high classification accuracy correspond to fire recognition.

show abstract

Real-Time Robotic Grasping and Localization Using Deep Learning-Based Object Detection Technique

Cited by 12 publications

References 22 publications

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

A Method for Target Detection Based on Synthetic Samples of Digital Twins

Indoor Automated Fire Extinguisher System using Computer Vision

Contact Info

Product

Resources

About