scholarly journals Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7074
Author(s):  
Chao-Ching Ho ◽  
Wei-Chi Chou ◽  
Eugene Su
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Embedded System ◽  
Network Model ◽  
Defect Detection ◽  
Network Optimization ◽  
Network Architecture ◽  
Bayesian Optimization ◽  
Original Network ◽  
Network Pruning

This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects.

scholarly journals Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1504
Author(s):  
Mingming Shen ◽  
Jing Yang ◽  
Shaobo Li ◽  
Ansi Zhang ◽  
Qiang Bai
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Dropout Rate ◽  
Adjustment Process ◽  
Correlation Model ◽  
Mathematical Correlation ◽  
Verification Methods

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.

scholarly journals Image processing of Casting defects based on Convolutional neural network

2021 ◽  
Vol 2137 (1) ◽  
pp. 012059
Author(s):  
Bowen Wei ◽  
Weixin Gao
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Network Architecture ◽  
Casting Defect ◽  
Neural Network Architecture ◽  
Vision Inspection ◽  
Design Requirements ◽  
Metal Castings

Abstract At present, there are numerous losses caused by corrosion cracking of metal castings in engineering in China. In order to detect the possible defects of metal castings in engineering, the laser ultrasonic vision inspection technology is used to image the castings, and then the identification efficiency is low. In order to process these images efficiently and quickly, convolutional neural network image processing technology is introduced. According to the actual needs, a convolutional neural network architecture is designed to recognize images, and whether the architecture meets the requirements is verified. Experimental results show that the performance of the architecture meets the design requirements. Under the same conditions, this structure provides a solution for casting defect detection combined with artificial intelligence.

Design of Trustworthy Cyber-Physical-Social Systems with Discrete Bayesian Optimization

2021 ◽  
pp. 1-31
Author(s):  
Yan Wang
Keyword(s):  
Network Optimization ◽  
Network Architecture ◽  
Optimization Problem ◽  
Social Systems ◽  
Systems Design ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Security And Privacy ◽  
Quantitative Metrics ◽  
Share Information

Abstract Cyber-physical-social systems (CPSS) with highly integrated functions of sensing, actuation, computation, and communication are becoming the mainstream consumer and commercial products. The performance of CPSS heavily relies on the information sharing between devices. Given the extensive data collection and sharing, security and privacy are of major concerns. Thus one major challenge of designing those CPSS is how to incorporate the perception of trust in product and systems design. Recently a trust quantification method was proposed to measure trustworthiness of CPSS by quantitative metrics of ability, benevolence, and integrity. The CPSS network architecture can be optimized by choosing a subnet such that the trust metrics are maximized. The combinatorial network optimization problem however is computationally challenging. Most of the available global optimization algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information with. The applications of ability and benevolence metrics in design optimization of CPSS architecture are demonstrated.

scholarly journals Facial image denoising using AutoEncoder and UNET

2021 ◽  
Vol 3 (2) ◽  
pp. 89-96
Author(s):  
Milan Tripathi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Image Quality ◽  
Image Denoising ◽  
Network Architecture ◽  
Multiple Model ◽  
Facial Image ◽  
Neural Network Architecture ◽  
Noisy Images

Image denoising is a crucial topic in image processing. Noisy images are generated due to technical and environmental errors. Therefore, it is reasonable to consider image denoising an important topic to study, as it also helps to resolve other image processing issues. However, the challenge is that the classical techniques used are time-consuming and not flexible enough. This article compares the two major neural network architecture which looks promising to resolve this issues. The AutoEncoder and UNET is now the most researched subject in deep learning for image denoising. Multiple model architectures are designed, implement, and evaluated. The dataset is preprocessed and then it is used to train and test the model. It is clearly shown in this paper which model performs the best in this task by comparing both models using the most used parameters to evaluate image quality PSNR and SSIM.

scholarly journals Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi

2021 ◽  
Vol 8 (2) ◽  
pp. 15-19
Author(s):  
Julkar Nine ◽  
Rahul Mathavan
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Intelligent Vehicles ◽  
Driving Safety ◽  
Raspberry Pi ◽  
Traffic Light ◽  
Traffic Lights ◽  
Architecture Model

Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.

scholarly journals Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1056
Author(s):  
Yanan Guo ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Mei Gao
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Network Architecture ◽  
Rapid Development ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Great Success ◽  
Cloud Detection

Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

scholarly journals State of the Art in Defect Detection Based on Machine Vision

2021 ◽  
Author(s):  
Zhonghe Ren ◽  
Fengzhou Fang ◽  
Ning Yan ◽  
You Wu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Visual Inspection ◽  
State Of The Art ◽  
Image Acquisition ◽  
Detection Algorithms ◽  
Further Development

AbstractMachine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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