scholarly journals Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2699 ◽  
Author(s):  
Redhwan Algabri ◽  
Mun-Taek Choi
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Human Robot Interaction ◽  
Target Person ◽  
Single Shot ◽  
Color Feature ◽  
Mapping Algorithm ◽  
Deep Learning Algorithm ◽  
Illumination Changes

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

2020 ◽  
Vol 9 (3) ◽  
pp. 1498-1508
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Single Shot ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
Rgb Images ◽  
Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

scholarly journals A Robot Obstacle Avoidance Method based on Random Forest HTM Cortical Learning Algorithm

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 788-803
Author(s):  
Ahmed Mahdi Abdulkadium
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Obstacle Avoidance ◽  
Time Complexity ◽  
Learning Algorithm ◽  
Operation Time ◽  
Absolute Error ◽  
Classification Model ◽  
Forecasting Accuracy ◽  
Deep Learning Algorithm

Robotics mainly concern with the movement of robot with improvement obstacle avoidance, this issue is handed. It contains of a Microcontroller to process the data, and Ultrasonic sensors to detect the obstacles on its path. Artificial intelligence is used to predict the presence of obstacle in the path. In this research random forest algorithm is used and it is improved by RFHTMC algorithm. Deep learning mainly compromises of reducing the mean absolute error of forecasting. Problem with random forest is time complexity, as it involves formation of many classification trees. The proposed algorithm reduces the set of rules which is used for classification model, to improve time complexity. Performance analysis shows an significant improvement in results as compare to other deep learning algorithm as well as random forest. Forecasting accuracy shows 8% improvement as compare to random forest with 26% reduced operation time.

scholarly journals Transfer Learning via Artificial Intelligence for Guiding Implant Placement in the Posterior Mandible: an in vitro Study

2021 ◽  
Author(s):  
Yun Liu ◽  
Zhi-cong Chen ◽  
Chun-ho Chu ◽  
Fei-Long Deng
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
In Vitro Study ◽  
Angular Deviation ◽  
Single Shot ◽  
Implant Position ◽  
Deep Learning Algorithm ◽  
Group A

Abstract Background: To explore the capacity of a single shot multibox detector (SSD) and Voxel-to-voxel prediction network for pose estimation (V2V-PoseNet) based artificial intelligence (AI) system in automatically designing implant plan. Methods: 2500 and 67 cases were used to develop and pre-train the AI system. After that, 12 patients who missed the mandibular left first molars were selected to test the capacity of the AI in automatically designing implant plan. There were three algorithms-based implant positions. They are Group A, B and C (8, 9 and 10 points dependent implant position, respectively). The AI system was then used to detect the characteristic annotators and determine the implant position. For every group, the actual implant position was compared with the algorithm-determined ideal position. And global, angular, depth and lateral deviation were calculate. One-way ANOVA followed by Tukey’s test was performed for statistical comparisons. The significance value was set at P< 0.05. Results: Group C represented the least coronal (0.6638±0.2651, range: 0.2060 to 1.109 mm) and apical (1.157±0.3350, range: 0.5840 to 1.654 mm) deviation, the same trend was observed in the angular deviation (5.307 ±2.891°, range: 2.049 to 10.90°), and the results are similar with the traditional statistic guide.Conclusion: It can be concluded that the AI system has the capacity of deep learning. And as more characteristic annotators be involved in the algorithm, the AI system can figure out the anatomy of the object region better, then generate the ideal implant plan via deep learning algorithm.

scholarly journals Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kitsada Thadson ◽  
Sarinporn Visitsattapongse ◽  
Suejit Pechprasarn
Keyword(s):  
Refractive Index ◽  
Deep Learning ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Phase Retrieval ◽  
Learning Algorithm ◽  
Single Shot ◽  
Refractive Index Sensing ◽  
Deep Learning Algorithm

AbstractA deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

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