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Intelligent Diagnosis Method of Bearing Fault Based on SDP image Fusion 2

Intelligent Diagnosis Method of Bearing Fault Based on SDP I...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Liu, Leijia Chen, Shiyu Tang, Ziling Chongqing University CQU-UC JOINT CO-OP Institute Chongqing China

ISBN: (纸本)9798350331417

Aiming at the problem that the vibration signal obtained by a single sensor is difficult to fully characterize the bearing fault characteristics, this paper proposes a bearing fault diagnosis method based on multi-sensor fusion SDP image and deep residual network (ResNet). The method firstly converts the vibration signals obtained from the acceleration sensors arranged at the drive end and the fan end respectively into snowflake images by the symmetrized dot pattern (SDP), then fuses the snowflake images corresponding to the different acceleration sensors as the different channels of the RGB images, and finally uses the fused SDP images as the input to the deep residual network (ResNet) to realise the fault diagnosis of the bearings. After experimental verification, the fault diagnosis accuracy of the proposed method can reach 98.8%, which is much higher than the fault diagnosis accuracy using a single sensor. Therefore, compared with the traditional single-sensor fault diagnosis, the multi-sensor SDP image fusion method proposed in this paper can more comprehensively characterize the bearing fault characteristics, and has better fault diagnosis effect. © 2023 IEEE.

关键词： bearing fault diagnosis multi-sensor fusion residual network symmetrical point mode

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Detection Method of Egg Shell Crack Based on image processing

Detection Method of Egg Shell Crack Based on Image Processin...

引用

2023 International conference on image, Signal processing, and Pattern Recognition, ISPP 2023

作者： Li, Fei Zhang, Ning College of Mechanical Engineering Xijing University Shaanxi Province Xi'an City China

ISBN: (纸本)9781510666351

As eggs are a very important food source in our daily diet, the inspection of egg quality must be strict. In this paper, a detection method based on machine vision image processing is proposed for the detection of eggshell cracks. Through image pre-processing, the images of the three BGR channels are compared, and then the median filtering is used to derive the image information of the R channel that is more favorable for detection. The image is enhanced to find the ROI, and connected region and to perform image segmentation to get the crack information. Finally, a morphological operation is used to extract the feature value of crack length from the image and conclude that the crack in the eggshell is detected by judging the feature value of the image. The experiments show that the accuracy of the OpenCV image processing-based detection method can reach more than 90%, which indicates that the method of detecting eggshells through image processing has unlimited potential. © 2023 SPIE.

关键词： image enhancement

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Rice Quality Analysis Based on Physical Attributes Using image processing and machine Learning 1

Rice Quality Analysis Based on Physical Attributes Using Ima...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Yashaswini, S. Dubey, Pushpalatha Rajesh Kumar, S. Cambridge Institute of Technology Department of Cse Bangalore India

ISBN: (纸本)9798350348057

This paper proposes a methodology to automate the quality control of rice grain, as its been manually analysed by veteran rice inspector which are inaccurate, collection of data of various rice types to analyse quality based on their physical attributes such as Area of the grain, Perimeter, Width and Height, Aspect Ratio, Major and Minor Axis. We have used techniques such as Digital image processing, Computer vision - which involves pre-processing, morphological operation, edge detection, object detection and finally object measurement. Training of the system involving manual and machine Learning which involves feeding of all data results from image processing into CSV file and formulating hypothesis for manual and SVM for machine based training, and then test to classify the quality into higher or lower grades based on the two methods and concluding the best methodology from observation. © 2023 IEEE.

关键词： Support vector machines

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Smart Object Detection using ESP32-CAM based on YOLO Algorithm 2

Smart Object Detection using ESP32-CAM based on YOLO Algorit...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Narwaria, Ravindra Pratap Ahirwar, Anand Prajapati, Abhinay K. Kumar, Abhishek Tiwari, Arin K. MITS Dept Elect Engn Gwalior MP India

ISBN: (纸本)9798331540661;9798331540678

In the present research work, a system is developed that can detect objects in real-time using a combination of the ESP32 CAM module and Python programming. The goal was to show how affordable hardware and free software can be used to make a system that recognizes objects quickly and accurately. By using computer vision and machine learning tricks, the proposed system can figure out the different objects with great precision. The setup process involves putting some code onto the ESP32 CAM module, finding its IP address, and then making it work smoothly with Python. The proposed system was tested in different situations, like watching for things in surveillance, making tasks easier with automation, and helping out in assistive technologies.

关键词： ESP32-CAM Module Computer vision Real-time Object Detection machine Learning (ML) Surveillance Systems Object Recognition Python Programming image processing YOLO Open CV

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Performance Evaluation of Various Histogram Equalization Techniques on Thermal images 2

Performance Evaluation of Various Histogram Equalization Tec...

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2nd IEEE International conference on Computer vision and machine Intelligence, CVMI 2023

作者： Singh, Shivani Sahil, Mohd. Kumar, Basant Singh, Himanshu Motilal Nehru National Institute of Technology Allahabad Dept. of Electronics and Communication Engineering Prayagraj India Drdo Irde Dehradun India

ISBN: (纸本)9798350305142

This paper presents performance evaluation of various pre-processing techniques specifically on thermal images. Since thermal cameras often have lower resolution than RGB cameras, it might be difficult to successfully use their data for object recognition tasks. In the proposed work, the digital image processing-based techniques are applied on the thermal images to enhance the quality. The histogram equalization techniques along with their different variants, such as contrast limited adaptive histogram equalization, adaptive histogram equalization, plateau limit histogram equalization, brightness preserving bi-histogram equalization and minimum mean brightness error bi-histogram equalization (MMBEBHE) are utilized for the pre-processing. The performance is evaluated by computing the different parameters such as edge detection entropy score (EDES), peak signal to noise ratio (PSNR) & blind reference less image spatial quality evaluator (BRISQUE), along with the computation time on various sets of images. It is observed that the MMBEBHE gives us better result with the lowest BRISQUE score and highest EDES score. © 2023 IEEE.

关键词： image enhancement

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Sea Surface Small Target Detection Method Based on Improved YOLOv5 2

Sea Surface Small Target Detection Method Based on Improved ...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Wen, Huiying Tang, Jianzhong Lu, Mingyang Lv, Xiaowen Zhejiang University Ocean College Hangzhou China Zhejiang University School of Mechanical Engineering Hangzhou China

ISBN: (纸本)9798350331417

At present, a complete and feasible detection system for sea surface targets has not yet been formed. Weak and small targets appear more frequently in sea scenes, and there is a lack of targeted algorithms to achieve better detection and identification. Therefore, this paper proposes a sea surface small target detection method based on improved YOLOv5. First, a data set of small targets on the sea surface is constructed, and the YOLOv5 model is selected to achieve target detection;then the sea antenna detection algorithm, attention module SE, CBAM, CA, feature fusion module BiFPN, and loss function EIoU are used to optimize the data, model, and strategy respectively. Finally, model training and verification testing are carried out. Experimental results show that compared with the baseline model (YOLOv5), the improved YOLOv5-sea-sky-line model has improved precision, recall, and average precision by 2.8%, 1.6%, and 0.8% respectively. © 2023 IEEE.

关键词： image processing sea antenna detection small target target detection YOLOv5

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Scratch Detection Method of Aircraft Skin Based on machine vision 3

Scratch Detection Method of Aircraft Skin Based on Machine V...

引用

3rd IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Nie, Peng Yue, Xingzuo Yu, Jiahe Zhang, Hui Shenyang Aerospace University Shenyang China Shenyang Aircraft Corporation Shenyang China

ISBN: (纸本)9798350398410

Aiming at the problems of low precision and slow speed of aircraft skin scratch detection, a method of aircraft skin scratch detection based on machine vision is proposed. Firstly, the image is enhanced by image multiplication, and the region of interest is selected by combining mean filtering and local threshold method to analyze the scratch image. Then the noise is filtered by threshold segmentation and morphological processing to screen the scratch area. Finally, the line Gaussian operator is used to optimize the parameter selection of the line extraction algorithm, and the union collinear contour operator is used to improve the scratch extraction quality and accurately obtain the scratch length information. The depth information of the area around the scratch and the scratch area is obtained through the depth map and morphological processing of the equipment, and the scratch depth value is calculated. The experiment proves that the detection method has small error, fast speed, good stability and detection accuracy of 30μm. © 2023 IEEE.

关键词： Feature extraction

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A Computer vision -Based Solution for Assessing Prosthetic Leg Length Discrepancy 12

A Computer Vision -Based Solution for Assessing Prosthetic L...

引用

12th International conference on e Health and Bioengineering

作者： Zvoristeanu, Otilia Fodor, Dirnitrie-Cristian Manta, Vasile-Ion Seghedin, Neculai-Eugen Achirei, Stefan-Daniel Chitariu, Dragos-Florin Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Dept Comp Sci & Engn Iasi Romania Dr Iacob Czihac Mil Emergency Clin Hosp Iasi Romania Gheorghe Asachi Tech Univ Iasi Fac Machine Mfg & Ind Management Dept Digital Prod Syst Iasi Romania

ISBN: (纸本)9798331532154;9798331532147

This paper presents a novel computer vision -based approach for assessing leg length discrepancy (LLD) in individuals with prosthetic limbs. The proposed solution uses image processing techniques to detect markers placed on the patient's knee, prosthesis, and a reference wall, allowing for precise measurement of limb alignment. Through a comparative analysis of the initial reference position, set by a specialist, and the current limb positioning, the algorithm identifies discrepancies in leg length. The system employs a non-invasive methodolog-y, utilizing an IP camera to capture images and communicate them via Wi-Fi to a computing unit for further analysis. Experimental validation, conducted on simulated LLDs ranging from lmm to 10mm, demonstrates the system's high sensitivity and accuracy in detecting subtle changes in limb alignment. This approach offers a scalable, automated alternative to traditional manual methods, improving both the reliability and ease of prosthetic adjustments.

关键词： leg length discrepancy prosthesis computer vision

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Automatic machine Learning based Real Time Multi-Tasking image Fusion 24

Automatic Machine Learning based Real Time Multi-Tasking Ima...

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16th International conference on machine Learning and Computing (ICMLC)

作者： Karim, Shahid Tong, Geng Li, Jinyang Yu, Xiaochang Hao, Jia Yu, Yiting Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Coll Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Micro Nano Syst Aerosp Minist Educ Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Prov Key Lab Micro & Nano Elect Mech Syst Xian 701172 Peoples R China

ISBN: (纸本)9798400709234

Imaging systems work diversely in the image processing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer vision applications. The multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multimodal (MM) image fusion. The careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. Then, the source images are fused with the pre-trained and fine-tuned models. The experimental results authenticate the consequences of our proposed approach compared to generic approaches.

关键词： automatic ML multi-tasking image fusion imaging systems

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On Comparing Multiple Approaches for Classifying Vessels in imagery 6

On Comparing Multiple Approaches for Classifying Vessels in ...

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conference on Artificial Intelligence and machine Learning for Multi-Domain Operations Applications VI

作者： Warner, John G. Patel, Vishal M. US Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781510674219;9781510674202

Deep learning-based approaches, such as Convolutional Neural Nets (CNNs), have shown high performance in classifying contents of images. CNNs, however, have the notable drawbacks of potentially high computing costs, poor explainability, and wide performance variance if the underlying imagery data deviates from the training baseline. As advanced image processing capabilities are matured, the on-board detection of objects in space-based imagery is increasingly proposed. On-board satellite processing applications, which may be resource-limited, can drive the need for simpler models that reduce the necessary computing burden for edge computing applications. This raises the question of how well classic computer vision techniques can compete with more modern approaches. This paper characterizes and compares the performance of multiple computer vision models for the application of distinguishing maritime vessels from typical clutter in commercial electrooptical (EO) satellite imagery. A Support Vector machine (SVM) model using manually curated features is compared to multiple DL-based models spanning a range of model sizes, with the goal of determining whether classical approaches can compete favorably with DL when computational resources are taken into consideration. Differences in performance and processing resources are characterized between the approaches. Findings include that the SVM-based model may approach the accuracy of some CNN-based models for classifying images of clouds in satellite EO imagery for smaller DL-based models. However, even the smallest DL-based models, which take about the same computational resources as the SVM-based model, generally out-perform the SVMbased model. This finding may have implications for the operational use of on-board processing techniques for satellite payloads.

关键词： CNN spacecraft onboard processing Automatic Target Recognition maritime

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