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 A RASPBERRY PI SELF-DRIVING CART BASED ON OPENCV AND DEEP LEARNING .

2021 ◽  
Vol 10 (3) ◽  
pp. 61-67
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Traffic Signal ◽  
The Self ◽  
Raspberry Pi ◽  
Signal Recognition ◽  
Ultrasonic Sensors ◽  
Better Than

The self-driving trolley created in this thesis uses cameras and ultrasonic sensors to obtain roadway information, and a deep learning based target recognition algorithm to find out which are the targets in the data obtained, so that the trolley can drive itself on a simulated roadway with functions such as obstacle avoidance and traffic signal recognition. Originally the car used a Raspberry Pi 3b+, but here the jetson nano, which is better than the Raspberry Pi 3b+, is used to implement it.

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.

Selective Attention and Error Processing in an Illusory Conjunction Task

2005 ◽  
Vol 19 (3) ◽  
pp. 216-231 ◽  
Author(s):  
Albertus A. Wijers ◽  
Maarten A.S. Boksem
Keyword(s):  
Target Detection ◽  
Event Related Potentials ◽  
Illusory Conjunction ◽  
Conjunction Error ◽  
Error Related Negativity ◽  
Display Element ◽  
Related Potentials ◽  
Feature Error ◽  
High Level ◽  
Correct Target

Abstract. We recorded event-related potentials in an illusory conjunction task, in which subjects were cued on each trial to search for a particular colored letter in a subsequently presented test array, consisting of three different letters in three different colors. In a proportion of trials the target letter was present and in other trials none of the relevant features were present. In still other trials one of the features (color or letter identity) were present or both features were present but not combined in the same display element. When relevant features were present this resulted in an early posterior selection negativity (SN) and a frontal selection positivity (FSP). When a target was presented, this resulted in a FSP that was enhanced after 250 ms as compared to when both relevant features were present but not combined in the same display element. This suggests that this effect reflects an extra process of attending to both features bound to the same object. There were no differences between the ERPs in feature error and conjunction error trials, contrary to the idea that these two types of errors are due to different (perceptual and attentional) mechanisms. The P300 in conjunction error trials was much reduced relative to the P300 in correct target detection trials. A similar, error-related negativity-like component was visible in the response-locked averages in correct target detection trials, in feature error trials, and in conjunction error trials. Dipole modeling of this component resulted in a source in a deep medial-frontal location. These results suggested that this type of task induces a high level of response conflict, in which decision-related processes may play a major role.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

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.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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