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LV-YOLO: logistic vehicle speed detection and counting using deep learning based YOLO network

SIGNAL image AND VIDEO processing 2024年第10期18卷 7419-7429页

作者： Rani, N. Gopika Priya, N. Hema Ahilan, A. Muthukumaran, N. PSG Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India PSG Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Commun Engn Tirunelveli Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India

In the era of smart cities and advancing transportation technologies, predicting logistic vehicle and vehicle speed is pivotal to enhancing traffic management, safety, and overall transportation efficiency. Properly predicting vehicle and vehicle speed is critical to the interests of both road users and traffic authorities. However, accurately predicting the vehicle speed and logistics vehicle of a single trip is a difficult task. In some cases, unpredicted accidents will happen, so death cases will increase. To overcome these issues, a novel Logistic Vehicle speed detection using the YOLO (LV-YOLO) method has been introduced to detect logistical vehicles and speed using the YOLO network. The proposed framework is divided into three layers such as image acquisition, segmentation layer, and detection layer. In the image acquisition layer, a CCTV camera captures highway traffic video. The collected video is converted into frames. In the segmentation layer, the video frame is segmented using U-Net, which segments the vehicle in the video frames. The detection layer performs truck detection, and speed detection using LV-YOLO on segmented frames based on the Boxy Vehicle dataset. The simulated results show that the LV-YOLO technique maintains excellent mAP levels of 99.42%. The LV-YOLO improves the overall mAP by 1.72, 5.42, and 0.82% better than the Simple Vehicle Counting System, real-time Detection, and Advance YOLOv3 Model for vehicle detection, 4.81, and 2.63% better than deep learning and CAN protocol, and 1D-CNN speed estimation mode for speed prediction respectively.

关键词： Vehicle speed Detection deep learning U-Net YOLO

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Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 9936-9944页

作者： De Sousa, Kevin Pilikos, Georgios Azcueta, Mario Floury, Nicolas European Space Agcy ESA European Space Res & Technol Ctr ESTEC Radio Frequency Payloads & Technol Div Wave Interact & Propagat SectElect Dept NL-2200 AG Noordwijk Netherlands Univ Toulouse CNRS Ctr Etud Spatiales Biosphare CESBIO CNESIRDINRAEUT3 Toulouse France European Space Agcy ESA European Space Res & Technol Ctr ESTEC Copernicus Sentinel Payload Sect Copernicus Space Component Space SegmentEarth Observat Projects De NL-2200 AG Noordwijk Netherlands

Synthetic aperture radar (SAR) is an indispensable tool for marine monitoring. Conventional data processing involves data down-linking and on-ground operations for image focusing, analysis, and ship detection. These steps take significant amount of time, resulting in potentially critical delays. In this work, we propose a ship detection algorithm that operates directly on raw SAR echoes, based on convolutional neural networks. To evaluate our approach, we performed experiments using raw data simulations and real raw SAR data from Sentinel-1 stripmap mode scenes. Preliminary results on this set show the capability of detecting multiple ships from raw data with similar accuracy as using single-look-complex images as input. Simultaneously, running time is reduced significantly, by-passing the image focusing step. This illustrates the great potential of deep learning, moving toward more intelligent SAR systems.

关键词： Marine vehicles Synthetic aperture radar Data models deep learning image segmentation Focusing European Space Agency raw data ship detection synthetic aperture radar (SAR)

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Mob-psp: modified MobileNet-V2 network for real-time detection of tomato diseases

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JOURNAL OF real-time image processing 2024年第5期21卷 181页

作者： Qiu, Hengmiao Yang, Jingmin Jiang, Juan Zhang, Wenjie Minnan Normal Univ Sch Comp Sci Zhangzhou 363000 Fujian Peoples R China Fujian Prov Univ Key Lab Data Sci & Intelligence Applicat Zhangzhou 363000 Fujian Peoples R China Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan

Tomato, as an essential food crop, is consumed worldwide, and at the same time, it is susceptible to several diseases that lead to a reduction in tomato yield. Proper diagnosis of tomato diseases is required to increase the output of tomato crops. For this purpose, this paper proposes a tomato plant disease detection algorithm based on Pyramid Scene Parsing Network (PSPNet) and deep learning. First, the training data set is augmented with data to alleviate the data imbalance problem in each category, and then the augmented images are fed into the proposed Mob-PSP network for training. The proposed network utilizes the lightweight MobileNet-V2 model as the feature extraction technique while integrating the PSPNet module to enhance the network's detection performance. The aim is to effectively extract local and global features from plant disease images, which are being introduced in plant disease detection. This study evaluated the model on the tomato subset of the public data set PlantVillage. The experimental results demonstrate that this algorithm achieves a balance between inference speed and detection accuracy, outperforming other state-of-the-art algorithms. Additionally, compared to the baseline model Inception-V3, the inference speed is improved by 10.73 frames per second, while maintaining an average accuracy of 99.69%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} with only 6.5M parameters.

关键词： Plant disease detection Pyramid scene parsing network MobileNet-V2 deep learning Convolutional neural network

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Efficient extraction of corn rows in diverse scenarios: A grid-based selection method for intelligent classification

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 218卷

作者： Quan, Longzhe Guo, Zhiming Huang, Lili Xue, Yi Sun, Deng Chen, Tianbao Geng, Tianyu Shi, Jianze Hou, Pengbiao He, Jinbin Lou, Zhaoxia Anhui Agr Univ Sch Engn Hefei 230036 Peoples R China Northeast Agr Univ Coll Engn Harbin 150030 Peoples R China

In various complex field environments, machine learning -based crop row detection faces challenges like rigidity and low adaptability. To address this issue, we integrated deep learning into agricultural analysis and established a diverse dataset of corn fields across various scenarios. By employing an end -to -end CNN model and predicting row and column anchors, we created a grid -like understanding of images, significantly streamlining the crop row detection process without the need for pixel -level segmentation. This innovative approach offers a novel method for comprehending the spatial structure of crop rows. Furthermore, we extended the concept of agricultural machinery movement core areas to our data annotation strategy, eliminating the need for pre -selecting ROI regions during crop row extraction. Experimental results demonstrate that our Row and Column Anchor Selection Classification (RCASC) method surpasses conventional approaches in terms of versatility, achieving an F1 score of 92.6 %. It can autonomously extract agricultural machinery movement areas, with video stream processing frame rates exceeding 100FPS and an average image processing time of approximately 10 ms. This method not only meets the real-time requirements for corn crop row recognition but also operates effectively in various special scenarios, offering a feasible solution for further advancing agricultural automation and precision.

关键词： Visual navigation deep learning Maize crop row detection Early corn crops image classification

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real-time Thermal Spray Process Monitoring Using Convolution Neural Network deep learning Architectures

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JOURNAL OF THERMAL SPRAY TECHNOLOGY 2024年第1期33卷 17-32页

作者： Malamousi, K. Delibasis, K. Kamnis, S. Univ Thessaly Dept Comp Sci & Bioinformat Lamia 35131 Greece Castolin Eutect Monitor Coatings Ltd Newcastle Upon Tyne NE29 8SE England

Thermal spray is essential for surface modification and coating of materials but is challenging to monitor in real-time due to high velocities, temperatures, and continuous torch or part movement. Existing static monitoring equipment cannot track the torch, making it difficult to ensure optimal process parameters. This study demonstrates that a specific convolutional neural network architecture, which divides the image into a grid and predicts bounding boxes and class probabilities for each cell, can accurately monitor High-Velocity-Oxy-Fuel thermal spray processes in real-time. The model is trained end-to-end to minimize errors in bounding box locations and class predictions, enabling one-pass object detection. This approach achieves up to 90% accuracy for all classes and processing speeds of up to 50 ms per frame on a modern smartphone, highlighting its potential as a practical, easily adoptable solution for real-time thermal spray process monitoring. This can have a significant impact on the thermal spray industry by enabling even small spray shops to achieve high-quality outcomes due to its simplicity and potential for wide adoption.

关键词： artificial intelligence coatings neural networks process monitoring thermal spray

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real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy

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MEDICAL PHYSICS 2023年第4期50卷 1962-1974页

作者： Waddington, David E. J. Hindley, Nicholas Koonjoo, Neha Chiu, Christopher Reynolds, Tess Liu, Paul Z. Y. Zhu, Bo Bhutto, Danyal Paganelli, Chiara Keall, Paul J. J. Rosen, Matthew S. S. Univ Sydney Image X Inst Fac Med & Hlth Sydney Australia Ingham Inst Appl Med Res Dept Med Phys Liverpool NSW Australia Massachusetts Gen Hosp AA Martinos Ctr Biomed Imaging Charlestown MA USA Boston Univ Dept Biomed Engn Boston MA USA Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Harvard Univ Dept Phys Cambridge MA USA Harvard Med Sch Boston MA USA

BackgroundMRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation. PurposeOnce trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep-learning-based image reconstruction for real-time tracking applications on MRI-Linacs. MethodsWe use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from imageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction. ResultsAUTOMAP models fine-tuned on retrospectively acquired lung cancer patient data reconstructed radial k-space with equivalent accuracy to CS but with much shorter processing times. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy. ConclusionAUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to alternative approaches for real-time image guidance applications.

关键词： deep learning MRI radiotherapy

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A deep learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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image Classification with Regularized Likelihood Estimation and Large Interval Discriminant Dimensionality Reduction

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2025年第2期41卷 385-400页

作者： Shen, Ying Zhu, Wei-hua Fu, Si-yong Wu, Guang-sheng Xinyu Univ Zhongxing Commun & Informat Coll Xinyu 338004 Peoples R China Xinyu Univ Sch Math & Comp Sci Xinyu 338004 Peoples R China

With the expansion of image application scale and the development of artificial intelligence industry, the demand for high precision and high robustness of image feature expression is gradually increasing. Convolutional neural networks (CNNS) have attracted much attention in the field of computer vision in recent years because of their excellent image feature learning ability. In order to further improve the accuracy and robustness of image classification, a second-order statistical convolutional neural network with multilayer fusion and cross-convolutional layer pooling are proposed in this study to obtain feature information of different dimensions in different convolutional layers. Due to the increase of computing and storage costs, an image classification method based on robust covariance of deep convolution features is proposed, and two regularized maximum likelihood estimators are introduced. Finally, the proposed method is tested by fine-grained experiments, image retrieval experiments and image classification experiments. The results show that MFBP (4,5) structure achieves 92.4%, 74.2% and 72.9% accuracy in finegrained image classification. Compared with MFBP (3,4,5), despite a slight decrease in accuracy, FPS performance is improved by more than 40%, demonstrating the advantages of this method in real-time applications. In addition, RMIE-VND and RMIE-IM methods improved by 4.1%, 8.4%, 7.8% and 7.1%, 12%, 9.8%, respectively, compared with LW methods on different data sets. The experimental results of image classification show that MFBP+RLME (VND+IM) has better performance and applicability than the current mainstream classification methods. This research is of great value for improving the efficiency and accuracy of image processing in big data environment, and provides a new research direction and practical tool for future real-time image application and computer vision system development.

关键词： image classification image dimensionality reduction convolutional neural net- work bilinear pooling regularized likelihood estimation

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YOLOv7-Rep: a re-parameterization method for surface defect detection in workpieces

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JOURNAL OF real-time image processing 2025年第1期22卷 1-13页

作者： Xu, Zhigang Fang, Pengwei Yang, Xinyu Wei, Pengjuan Lanzhou Univ Technol Sch Comp & Commun Pengjiaping Rd Lanzhou 730050 Gansu Peoples R China

The main goal of workpiece surface defect detection is to improve accuracy and real-time performance. Generally, the practice of using a network model with stronger representation ability and performing lightweight design at the same time cannot solve the problem of better performance balance between model training and inference phases. To address this, this paper optimizes and proposes a structural re-parameterization defect detection method: YOLOv7-Rep. First, we proposed a re-parameterization convolutional module: RepC2, which is a new block with cross-stage partial (CSP) connection and efficient gradient flow branch structure. Second, this article integrates the RepC2 module based on YOLOv7 and incorporates the coordinate attention (CA) mechanism in the backbone. Finally, we introduce the WIoUv1 bounding-box regression loss function. Experimental analysis demonstrates that YOLOv7-Rep outperforms object detectors with the same parameter count in terms of both detection accuracy and speed. It achieves detection accuracies of 78.3%, 87.3%, and 83.5% on three industrial component datasets (NEU-DET, TCAP-DET, and GC10-DET), respectively. Compared to YOLOv7, it significantly improves detection frame rates by 4.9% (an increase of 11.3 FPS), while achieving a better performance balance between training and inference phases in surface defect detection tasks.

关键词： deep learning Computer vision Object detection Workpiece surface defects YOLOv7 Structural re-parameterization

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Multi-source data-driven approach for prediction of melt density during polymer compounding

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POLYMER ENGINEERING AND SCIENCE 2024年第6期64卷 2627-2639页

作者： Zhang, Bin-Bin Chen, Zhu-Yun Zhang, Fei Jin, Gang South China Univ Technol Natl Engn Res Ctr Novel Equipment Polymer Proc Key Lab Polymer Proc Engn Minist Educ Guangzhou Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China

Melt density is a crucial quality indicator for polymer composites, yet real-time measurement remains challenging due to processing complexities. While existing machine learning methods offer solutions, they often fall short in complex compounding scenarios. This study presents a novel multi-source data-driven approach for measuring melt density in polycarbonate/acrylonitrile butadiene styrene blends. By incorporating ultrasonic, near-infrared, and Raman spectra data acquired during melt processing, a deep separable convolutional neural network model is developed to predict melt density accurately. The model effectively fuses multi-source data to establish the mapping relationship between input data and melt density output. Results demonstrate the model's ability to monitor melt density in real-time, achieving a prediction accuracy with RMSE and R2 indexes of 0.005 g/cm3 and 0.9841, respectively. The proposed approach outperforms existing methods, showcasing its effectiveness and superiority in melt density prediction for polymer compounding *** Establishment of the real-time monitoring system for polymer extrusion processes. Conversion of multi-sensor signals into time-frequency images using wavelet decomposition. Fusion of sensor data into a three-channel tensor-image. Development of a data-driven DSCNN model for predicting melt density. Implementation for online monitoring and prediction in PC/ABS compounding system. Sensor data acquisition and DSCNN model monitoring process diagram. image

关键词： data fusion data-driven density measurement DSCNN extrusion processing in-line monitoring polymer melt

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