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Target Recognition of Industrial Robots Using Machine Vision in 5G Environment.

Zhenkun Jin¹, Lei Liu², Dafeng Gong³

¹Department of Information Engineering, Wuhan Business University, Wuhan, China.

Frontiers in Neurorobotics

|March 15, 2021

Summary

This summary is machine-generated.

This study enhances industrial robot visual recognition using deep learning (DL) and convolutional neural networks (CNNs) in 5G environments. The improved VGG-16 model achieves 82.34% accuracy for object detection and positioning.

Keywords:

5G environment artificial intelligence deep learning industrial robot machine vision

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Industrial robots require precise object detection for efficient operation.
Current systems face challenges with positioning errors, slow recognition, and low accuracy, especially in 5G environments.
Deep learning (DL) offers potential solutions for enhancing visual recognition capabilities.

Purpose of the Study:

To address limitations in industrial robot detection, including large positioning errors and low recognition accuracy.
To optimize industrial robot visual recognition systems using improved deep learning algorithms.
To evaluate the effectiveness of enhanced Fast-RCNN and VGG-16 models for target detection and classification.

Main Methods:

Implemented a convolutional neural network (CNN) model for image convolution, pooling, and target classification.
Utilized an improved Fast-RCNN model for detecting bottled objects.
Employed an improved VGG-16 classification network with a Hyper-Column scheme for small objects in complex environments.
Compared simulation results with other advanced CNN algorithms.

Main Results:

The improved Fast-RCNN and VGG-16 models achieved a recognition accuracy rate of 82.34%.
Both models demonstrated superior performance in positioning and recognizing targets compared to other advanced CNN algorithms.
The improved VGG-16 network with the Hyper-Column scheme showed significant accuracy and effectiveness.

Conclusions:

The improved VGG-16 classification network based on the Hyper-Column scheme provides accurate and effective target recognition and positioning for industrial robots.
This approach offers a valuable experimental reference for the application and development of industrial robots in 5G environments.
Enhanced deep learning models can overcome existing challenges in industrial robot visual perception.