Deep Reinforcement Learning Enhanced Convolutional Neural Networks for Robotic Grasping

2021 ◽  
Author(s):  
Jianhao Fang ◽  
Weifei Hu ◽  
Chuxuan Wang ◽  
Zhenyu Liu ◽  
Jianrong Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Success Rate ◽  
Industrial Applications ◽  
Virtual Space ◽  
Robotic Manipulation ◽  
Robotic Grasping ◽  
The Neural Network ◽  
Virtual Grasping ◽  
Unseen Objects

Abstract Robotic grasping is an important task for various industrial applications. However, combining detecting and grasping to perform a dynamic and efficient object moving is still a challenge for robotic grasping. Meanwhile, it is time consuming for robotic algorithm training and testing in realistic. Here we present a framework for dynamic robotic grasping based on deep Q-network (DQN) in a virtual grasping space. The proposed dynamic robotic grasping framework mainly consists of the DQN, the convolutional neural network (CNN), and the virtual model of robotic grasping. After observing the result generated by applying the generative grasping convolutional neural network (GG-CNN), a robotic manipulation conducts actions according to Q-network. Different actions generate different rewards, which are implemented to update the neural network through loss function. The goal of this method is to find a reasonable strategy to optimize the total reward and finally accomplish a dynamic grasping process. In the test of virtual space, we achieve an 85.5% grasp success rate on a set of previously unseen objects, which demonstrates the accuracy of DQN enhanced GG-CNN model. The experimental results show that the DQN can efficiently enhance the GG-CNN by considering the grasping procedure (i.e. the grasping time and the gripper’s posture), which makes the grasping procedure stable and increases the success rate of robotic grasping.

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR DETECTION OF MELANOMA USING SKIN LESION IMAGE ON MOBILE DEVICE

2021 ◽  
Vol 3 (1) ◽  
pp. 8-14
Author(s):  
D. V. Fedasyuk ◽  
◽  
T. V. Demianets ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Mobile Device ◽  
Mobile Application ◽  
High Accuracy ◽  
File Size ◽  
Melanoma Detection ◽  
The Neural Network ◽  
Classification Time

A melanoma is the deadliest skin cancer, so early diagnosis can provide a positive prognosis for treatment. Modern methods for early detecting melanoma on the image of the tumor are considered, and their advantages and disadvantages are analyzed. The article demonstrates a prototype of a mobile application for the detection of melanoma on the image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

scholarly journals Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 972 ◽  
Author(s):  
Xingchen Liu ◽  
Qicai Zhou ◽  
Jiong Zhao ◽  
Hehong Shen ◽  
Xiaolei Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Industrial Applications ◽  
Experimental Result ◽  
Noisy Environment ◽  
Experimental Conditions ◽  
Global Average ◽  
Noisy Input ◽  
Convolutional Autoencoder

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

Domain Randomization for Detection and Position Estimation of Multiples of a Single Object With Applications to Localizing Bolts on Structures

2019 ◽  
Author(s):  
Ezra Ameperosa ◽  
Pranav A. Bhounsule
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Position Estimation ◽  
Learning Approaches ◽  
Single Object ◽  
Automated Method ◽  
The Neural Network ◽  
Periodic Maintenance ◽  
Camera Position ◽  
Periodic Replacement

Abstract Periodic replacement of fasteners such as bolts are an integral part of many structures (e.g., airplanes, cars, ships) and require periodic maintenance that may involve either their tightening or replacement. Current manual practices are time consuming and costly especially due to the large number of bolts. Thus, an automated method that is able to visually detect and localize bolt positions would be highly beneficial. In this paper, we demonstrate the use of deep neural network using domain randomization for detecting and localizing multiple bolts on a workpiece. In contrast to previous deep learning approaches that require training on real images, the use of domain randomization allows for all training to be done in simulation. The key idea here is to create a wide variety of computer generated synthetic images by varying the texture, color, camera position and orientation, distractor objects, and noise, and train the neural network on these images such that the neural network is robust to scene variability and hence provides accurate results when deployed on real images. Using domain randomization, we train two neural networks, a faster regional convolutional neural network for detecting the bolt and predicting a bounding box, and a regression convolutional neural network for estimating the x- and y-position of the bolt relative to the coordinates fixed to the workpiece. Our results indicate that in the best case we are able to detect bolts with 85% accuracy and are able to predict the position of 75% of bolts within 1.27 cm. The novelty of this work is in the use of domain randomization to detect and localize: (1) multiples of a single object, and (2) small sized objects (0.6 cm × 2.5 cm).

scholarly journals A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bruce Lim ◽  
Ewen Bellec ◽  
Maxime Dupraz ◽  
Steven Leake ◽  
Andrea Resta ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffraction Pattern ◽  
Three Dimensional ◽  
X Ray Diffraction ◽  
Coherent Diffraction Imaging ◽  
The Neural Network ◽  
Diffraction Imaging ◽  
Diffraction Patterns ◽  
Defect Recognition

AbstractCoherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials. These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type (screw or edge). Our study paves the way for defect-recognition in 3D coherent diffraction patterns for material science.

Sign Board Recognition Based on Convolutional Neural Network Using Yolo-3

2020 ◽  
Vol 17 (8) ◽  
pp. 3478-3483
Author(s):  
V. Sravan Chowdary ◽  
G. Penchala Sai Teja ◽  
D. Mounesh ◽  
G. Manideep ◽  
C. T. Manimegalai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Road Accidents ◽  
Learning Models ◽  
The Neural Network

Road injuries are a big drawback in society for a few time currently. Ignoring sign boards while moving on roads has significantly become a major cause for road accidents. Thus we came up with an approach to face this issue by detecting the sign board and recognition of sign board. At this moment there are several deep learning models for object detection using totally different algorithms like RCNN, faster RCNN, SPP-net, etc. We prefer to use Yolo-3, which improves the speed and precision of object detection. This algorithm will increase the accuracy by utilizing residual units, skip connections and up-sampling. This algorithm uses a framework named Dark-net. This framework is intended specifically to create the neural network for training the Yolo algorithm. To thoroughly detect the sign board, we used this algorithm.

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