scholarly journals Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow

2021 ◽  
Vol 2066 (1) ◽  
pp. 012070
Author(s):  
Wencai Xu
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Optimization Algorithms ◽  
Recognition Algorithm ◽  
Parallel Execution ◽  
Parallel Optimization ◽  
Massive Data ◽  
Stable Model ◽  
Accuracy Rate ◽  
Learning Framework

Abstract Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.

scholarly journals A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Lijing Liu
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Target Identification ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Accuracy Rate ◽  
Intelligent Robots ◽  
Vision Sensors ◽  
Pyramid Model ◽  
High Level

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.

scholarly journals The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism

2021 ◽  
Vol 2066 (1) ◽  
pp. 012002
Author(s):  
Wencai Xu
Keyword(s):  
Deep Learning ◽  
Sequence Analysis ◽  
Video Sequence ◽  
Recognition Algorithm ◽  
Machine Intelligence ◽  
Software Library ◽  
Learning Mechanism ◽  
Learning Framework ◽  
Recognition Algorithms ◽  
Video Sequence Analysis

Abstract With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.

An Open IoHT-based Deep Learning Framework for Online Medical Image Recognition

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Carlos M. J. M. Dourado ◽  
Suane Pires P. Da Silva ◽  
Raul Victor M. Da Nobrega ◽  
Pedro P. Reboucas Filho ◽  
Khan Muhammad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Medical Image ◽  
Learning Framework ◽  
Medical Image Recognition

A deep learning-based image recognition algorithm for fecal shape of domestic rabbits

2018 ◽  
Vol 32 (s1) ◽  
pp. 67-78 ◽  
Author(s):  
Jingjing DEMOLOMBE ◽  
Tali YUAN ◽  
Xiao ZHANG ◽  
Longfei SHAO ◽  
Liheng GONG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Recognition Algorithm ◽  
Domestic Rabbits

scholarly journals Research on GPR image recognition based on deep learning

2020 ◽  
Vol 309 ◽  
pp. 03027
Author(s):  
Zhimin Gong ◽  
Huaiqing Zhang
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Ground Penetrating Radar ◽  
Simulation Software ◽  
Accuracy Rate ◽  
Data Set ◽  
Average Accuracy ◽  
Recognition Ability ◽  
Ground Penetrating ◽  
Traditional Image

It is difficult for traditional image recognition methods to accurately identify ground penetrating radar (GPR) images. This paper proposes a deep-learning based Faster R-CNN algorithm for the automatic classification and recognition of GPR images. Firstly, GPR images with different features were obtained by using gprMax, a professional GPR simulation software. Then, the feature of the target in the image was taken as the recognition object and the data set was made. Finally, Faster R-CNN’s recognition ability of GPR images was analyzed from various accuracy, average accuracy and other indicators. The results showed that Faster R-CNN could successfully identify GPR images and accurately classify them, with an average accuracy rate of 93.9%.

Weld Image Recognition Algorithm Based on Deep Learning

2019 ◽  
Vol 34 (08) ◽  
pp. 2052004
Author(s):  
Yan Li ◽  
Miao Hu ◽  
Taiyong Wang
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Weld Seam ◽  
Detection Method ◽  
Automatic Detection ◽  
Recognition Algorithm ◽  
Image Texture ◽  
Detection Accuracy ◽  
Weld Defects ◽  
Weld Inspection

As an important part of metal processing, welding is widely used in industrial manufacturing activities, and its application scenarios are very extensive. Due to technical limitations, the welding process always unavoidably leaves weld defects. Weld defects are extremely hazardous, and the work used must be guaranteed to be defect-free, regardless of the field. However, manual weld inspection has subjective factors such as inefficiency and easy missed detection, and although some automatic weld inspection methods have appeared, these traditional methods still do not meet actual demand in terms of detection time and detection accuracy. Therefore, there is a need for a higher quality weld image automatic detection method to replace the manual method and the traditional automatic detection method. In view of the above, this paper proposes a weld seam image recognition algorithm based on deep learning. The Adam adaptive moment estimation algorithm is chosen as the backpropagation optimization algorithm to accelerate the training of convolutional neural networks and design an independent adaptive learning rate. Through the simulation of the collected 4500 tube images, the adaptive threshold-based method is used for weld seam extraction. The algorithm proposed in this paper is compared with the weld seam recognition method based on image texture feature value distribution (ITFVD) and the SUSAN-based weld defect target detection method. The results show that the proposed method can identify weld defects in a short time on different sizes of weld images, and can further detect the type of weld defects. In addition, the method in this paper is better than the other two methods in the false detection rate, recall rate and overall recognition accuracy, which shows that the experimental results have achieved the expected results.

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