A novel image recognition algorithm of target identification for unmanned surface vehicles based on deep learning

2019 ◽  
Vol 37 (4) ◽  
pp. 4437-4447
Author(s):  
Wei He ◽  
Shuo Xie ◽  
Xinglong Liu ◽  
Tao Lu ◽  
Tianjiao Luo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Target Identification ◽  
Recognition Algorithm ◽  
Unmanned Surface Vehicles

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 ISAR Image Recognition Algorithm and Neural Network Implementation

2017 ◽  
Vol 17 (4) ◽  
pp. 183-199 ◽  
Author(s):  
A. Lazarov ◽  
C. Minchev
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Recognition ◽  
Network Architecture ◽  
Target Identification ◽  
Recognition Algorithm ◽  
Decision Strategy ◽  
Neural Network Architecture ◽  
Line Extraction ◽  
Radar Applications

AbstractThe image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

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 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.

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.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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