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A Real Time Quality Monitoring System for the Lighting Industry: A Practical and Rapid Approach Using computer vision and image processing (CVIP) Tools

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INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT 2011年第4期3卷 14-21页

作者： Ng, C. K. Wu, C. H. Ip, W. H. Chan, C. Y. Ho, G. T. S. Hong Kong Polytechn Univ Dept Ind & Syst Engn Hong Kong Peoples R China

In China, the manufacturing of lighting products is very labour intensive. The approach used to check quality and control production relies on operators who test using various types of fixtures. In order to increase the competitiveness of the manufacturer and the efficiency of production, the authors propose an integrated system. This system has two major elements: a computer vision system (CVS) and a real-time monitoring system (RTMS). This model focuses not only on the rapid and practical application of modern technology to a traditional industry, but also represents a process innovation in the lighting industry. This paper describes the design and development of the prototyped lighting inspection system based on a practical and fast approach using computer vision and imaging processing (CVIP) tools. LabVIEW with IMAQ vision Builder is the chosen tool for building the CVS. Experimental results show that this system produces a lower error rate than humans produce in the quality checking process. The whole integrated manufacturing strategy, aimed at achieving a better performance, is most suitable for a China and other labour intensive environments such as India.

关键词： Integrated manufacturing computer vision and image processing Real-time monitoring system LabVIEW IMAQ vision builder

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Surface Thermo-Dynamic Characterization of Poly (Vinylidene Chloride-Co-Acrylonitrile) (P(VDC-co-AN)) Using Inverse-Gas Chromatography and Investigation of Visual Traits Using computer vision image processing Algorithms

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POLYMERS 2020年第8期12卷 1631页

作者： Kakani, Vijay Kim, Hakil Basivi, Praveen Kumar Pasupuleti, Visweswara Rao Inha Univ Informat & Commun Engn 100 Inharo Incheon 22212 South Korea Sri Venkateswara Univ Dept Chem Tirupati 517502 Andhra Pradesh India Univ Malaysia Sabah Dept Biomed Sci & Therapeut Fac Med & Hlth Sci Sabah 88400 Kota Kinabalu S Malaysia Abdurrab Univ Fac Med & Hlth Sci Dept Biochem Jl Riau Ujung 73 Pekanbaru 28292 Riau Indonesia

The Inverse Gas Chromatography (IGC) technique has been employed for the surface thermo-dynamic characterization of the polymer Poly(vinylidene chloride-co-acrylonitrile) (P(VDC-co-AN)) in its pure form. IGC attributes, such as London dispersive surface energy, Gibbs free energy, and Guttman Lewis acid-base parameters were analyzed for the polymer (P(VDC-co-AN)). The London dispersive surface free energy (gamma SL) was calculated using the Schultz and Dorris-Gray method. The maximum surface energy value of (P(VDC-co-AN )) is found to be 29.93 mJ.m-2and 24.15 mJ.m-2in both methods respectively. In our analysis, it is observed that the gamma SLvalues decline linearly with an increase in temperature. The Guttman-Lewis acid-base parameterKa,Kbvalues were estimated to be 0.13 and 0.49. Additionally, the surface character S value and the correlation coefficient were estimated to be 3.77 and 0.98 respectively. After the thermo-dynamic surface characterization, the (P(VDC-co-AN)) polymer overall surface character is found to be basic. The substantial results revealed that the (P(VDC-co-AN)) polymer surface contains more basic sites than acidic sites and, hence, can closely associate in acidic media. Additionally, visual traits of the polymer (P(VDC-co-AN)) were investigated by employing computer vision and image processing (CVIP) techniques on Scanning Electron Microscopy (SEM) images captured at resolutions x50, x200 and x500. Several visual traits, such as intricate patterns, surface morphology, texture/roughness, particle area distribution (DA), directionality (DP), mean average particle area (mu avg) and mean average particle standard deviation (sigma avg), were investigated on the polymer's purest form. This collective study facilitates the researches to explore the pure form of the polymer Poly(vinylidene chloride-co-acrylonitrile) (P(VDC-co-AN )) in both chemical and visual perspective.

关键词： Poly(vinylidene chloride-co-acrylonitrile) (P(VDC-co-AN )) thermo-dynamic surface characterization surface free energy inverse gas chromatography visual traits computer vision and image processing

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A visual analytical method for evaluating tool flank wear volumes of micro-milling cutters with AKAZE features matching: A preliminary study

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WEAR 2025年 564卷

作者： Zhang, Yu Gao, Shuaishuai Duan, Xianyin Zhu, Kunpeng Wuhan Business Univ Sch Mech & Elect Engn Wuhan 430056 Hubei Peoples R China JiangHan Univ Sch Artificial Intelligence Wuhan 430056 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Hubei Peoples R China Chinese Acad Sci Inst Adv Mfg Technol Hefei Inst Phys Sci Lab Precis Mfg Changzhou 213164 Jiangsu Peoples R China

Tool wear is one of the most important factors restricting the development and application of micro-milling technology. In order to accurately evaluate the tool wear features in the micro-milling process, a visual analytical approach is proposed with multiple advanced image processing methods. Firstly, A theoretical model of tool wear volume considering the end concave angle is proposed, which is more descriptive of practical situations than the traditional one-dimensional parameters, the tool flank wear length. Secondly, the tool flank wear areas are accurately obtained of image segmentation with the Canny gradient operator, extraction with corner feature detection, and registration with Accelerated-KAZE (AKAZE) feature matching. Finally, the features of the tool flank wear are evaluated of correcting the integration areas with the Hough transformation, determining the parameters with scale conversion, and verifying the areas and volumes of the tool flank wear with experimental data and key quality metrics, Mean-Squared Error (MSE) and Multi-Scale Structural Similarity (MSSSIM). The results demonstrate that the method presented effectively characterises the tool flank wear, giving a suitable approach for computer vision and image feature processing of tool wear monitoring during machining.

关键词： Micro-milling Tool flank wear computer vision and image processing image registration AKAZE feature matching Hough transformation

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Motion vehicle detection for traffic video streams

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JOURNAL OF ENGINEERING-JOE 2025年第1期2025卷

作者： Song, Jianhao Ding, Hua Liu, Lai Chen, Juntao Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

This research offers an enhanced version of the YOLOv5s+MobileNetV3+BiFPN method to address a number of issues in moving vehicle recognition in traffic video streams, including lightweight network model, motion blur, scene delay etc. By substituting YOLOv5s BackBone with the lightweight MobileNetV3 backbone network and employing a parameter-free attention mechanism, the network model's parameters are reduced. To improve the ability of features to learn, use SimAM instead of SENet structure;Integrating the BiFPN network will increase the computing cost while improving the detection accuracy;Utilize the K-means++ method to improve the anchor frame choice and boost the detection performance for targets of various scales;In addition, the dataset is arbitrarily disregarded to address the issue of extreme information redundancy. Results are obtained by applying the enhanced algorithm to the processed video detection dataset. The experimental findings demonstrate that this method, when compared to the original YOLOv5s algorithm, has a faster detection speed and a higher detection accuracy in the detection of vehicle targets in traffic video streams. The mAP@0.5:0.95 improved by 11.23%, and the FPS increased from 15.87 to 47.62.

关键词： automatic vehicle location behavioural modelling computer vision computer vision and image processing condition monitoring image processing and machine vision numerical modelling traffic simulation video processing

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Machine learning-assisted anomaly detection for power line components: A case study in Pakistan

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JOURNAL OF ENGINEERING-JOE 2024年第7期2024卷

作者： Basit, Abdul Manzoor, Habib Ullah Akram, Muhammad Gelani, Hasan Erteza Hussain, Sajjad Univ Engn & Technol Dept Elect Engn Lahore Pakistan Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland

A continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto-encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto-encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts. Researchers in Pakistan utilized an unmanned aerial vehicle to collect 10,343 photos for automating the identification of defective power distribution components. They employed machine learning models, including VGG-16 with 99.00% accuracy and convolutional auto-encoder, to differentiate normal and abnormal parts. These models offer the potential for unmanned aerial vehicles to swiftly detect faults in power distribution systems. image

关键词： computer vision and image processing image classification neural network power system analysis transmission lines

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Intelligent Adaptive Object Recognition 12

Intelligent Adaptive Object Recognition

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Almazmome, Safa Zhang, Li Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England Northumbria Univ Dept Comp Sci & Digital Technol Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9781509040933

This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation. Experimental results indicate that the proposed system achieves high accuracy 98% accurate for real-time object recognition tasks.

关键词： Object recognition neural networks computer vision and image processing

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Real Time Hand Gesture Recognition for Human computer Interaction 6

Real Time Hand Gesture Recognition for Human Computer Intera...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Agrawal, Rishabh Gupta, Nikita Savitribai Phule Pune Univ Dept Comp Engn Army Inst Technol Pune Maharashtra India

ISBN: (纸本)9781467382861

Most of the human computer interaction interfaces that are designed today require explicit instructions from the user in the form of keyboard taps or mouse clicks. As the complexity of these devices increase, the sheer amount of such instructions can easily disrupt, distract and overwhelm users. A novel method to recognize hand gestures for human computer interaction, using computer vision and image processing techniques, is proposed in this paper. The proposed method can successfully replace such devices (e.g. keyboard or mouse) needed for interacting with a personal computer. The method uses a commercial depth + rgb camera called Senz3D, which is cheap and easy to buy as compared to other depth cameras. The proposed method works by analyzing 3D data in real time and uses a set of classification rules to classify the number of convexity defects into gesture classes. This results in real time performance and negates the requirement of any training data. The proposed method achieves commendable performance with very low processor utilization.

关键词： Human computer Interaction (HCI) gesture recognition hand gestures convex hull convexity defects computer vision and image processing

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Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high-content cellular imaging systems

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JOURNAL OF ENGINEERING-JOE 2023年第12期2023卷

作者： Pandey, Saroj Kumar Kumar, Ankit Yadav, Dhirendra Prasad Sinha, Anurag Hassan, Md. Mehedi Singh, N. K. Bhatnagar, Yash Garg, Namit GLA Univ Mathura Dept CEA Mathura India IGNOU Dept Comp Sci New Delhi India Khulna Univ Comp Sci & Engn Discipline Khulna Bangladesh Birla Inst Technol Dept Comp Sci Ranchi India Indian Inst Technol Dept Biotechnol & Biochem Engn Delhi India Indian Inst Technol Dept Math & Comp Kanpur India

This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non-threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real-time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.

关键词： computer vision computer vision and image processing

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BrutNet: A novel approach for violence detection and classification using DCNN with GRU

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JOURNAL OF ENGINEERING-JOE 2024年第4期2024卷

作者： Haque, Mahmudul Nyeem, Hussain Afsha, Syma Independent Univ Dept Comp Sci & Engn CSE Bangladesh IUB Dhaka Bangladesh Mil Inst Sci & Technol MIST Dept Elect Elect & Commun Engn EECE Dhaka Bangladesh Univ Girona Erusmus Mundus Joint Master Intelligent Field Robo Girona Spain Mirpur Cantonment Dhaka 1216 Bangladesh Dept EECE MIST Dhaka 1216 Bangladesh

Automatic Violence Detection and Classification (AVDC) with deep learning has garnered significant attention in computer vision research. This paper presents a novel approach for combining a custom Deep Convolutional Neural Network (DCNN) with a Gated Recurrent Unit (GRU) in developing a new AVDC model called BrutNet. Specifically, a time-distributed DCNN (TD-DCNN) is developed to generate a compact 2D representation with 512 spatial features per frame from a set of equally-spaced frames of dimension 160x$\times$90 in short video segments. Further to leverage the temporal information, a GRU layer is utilised, generating a condensed 1D vector that enables binary classification of violent or non-violent content through multiple dense layers. Overfitting is addressed by incorporating dropout layers with a rate of 0.5, while the hidden and output layers employ rectified linear unit (ReLU) and sigmoid activations, respectively. The model is trained on the NVIDIA Tesla K80 GPU through Google Colab, demonstrating superior performance compared to existing models across various video datasets, including hockey fights, movie fights, AVD, and RWF-2000. Notably, the model stands out by requiring only 3.416 million parameters and achieving impressive test accuracies of 97.62%, 100%, 97.22%, and 86.43% on the respective datasets. Thus, BrutNet exhibits the potential to emerge as a highly efficient and robust AVDC model in support of greater public safety, content moderation and censorship, computer-aided investigations, and law enforcement. BrutNet, an AVDC model combining a customized TD-DCNN with GRU, is presented that achieves exceptional accuracies ranging from 97.62% to 100% across diverse datasets. Notably, BrutNet demonstrates remarkable computational efficiency with only 3.416 million parameters, setting a new standard for efficient violence detection. The model's practical implications extend to enhancing public safety, content moderation, and law enforcement, marking a

关键词： behavioural modelling computer vision and image processing image processing and machine vision intelligent systems engineering monitoring

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Intelligent Adaptive Object Recognition

Intelligent Adaptive Object Recognition

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International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Safa Almazmome Li Zhang School of Computing Science Newcastle University Department of Computer Science and Digital Technologies Faculty of Engineering and Environment Northumbria University

ISBN: (纸本)9781509040940

关键词： Object recognition Neural networks computer vision and image processing

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