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Conference on real-time processing of image, Depth, and Video Information

作者： Egipko, V Zhdanova, M. Gapon, N. Voronin, V. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673199;9781510673182

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.

关键词： deep learning augmented reality computer vision object recognition robotic systems real-time processing

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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms

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DISCOVER FOOD 2025年第1期5卷 1-23页

作者： Ajith, S. Vijayakumar, S. Elakkiya, N. Int Rice Res Inst New Delhi 110012 India ICAR Indian Inst Rice Res Hyderabad 500030 India ICAR Cent Inst Brackishwater Aquaculture Chennai 600028 Tamil Nadu India

The growing demand for food grains amidst resource constraints necessitates advancements in crop management. Artificial intelligence, particularly machine learning and deep learning, is revolutionizing agricultural practices by enabling data-driven, precise, and sustainable solutions. This review synthesizes advancements in artificial intelligence applications across key domains, including crop yield prediction, precision irrigation, soil fertility mapping, insect pest and disease forecasting, and foodgrain quality assessment. Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. Artificial intelligence-driven tools automate pest detection and classification, optimize irrigation with minimal human input, generate high-resolution soil fertility maps, and enhance foodgrain quality assessment through rapid defect and contaminant detection. Artificial intelligence-powered precision irrigation integrates real-time soil moisture data and weather predictions for optimized water usage. Similarly, artificial intelligence-driven soil fertility mapping not only enables high-resolution assessments but also facilitates real-time monitoring of nutrient dynamics, supporting sustainable land management. In pest and disease detection, artificial intelligence systems combining image processing and real-time analytics demonstrate promise for early intervention. Artificial intelligence integration into foodgrain quality assessment leverages hyperspectral imaging and predictive models to enhance grading, adulteration detection, and contaminant screening, contributing to food safety and market competitiveness. Furthermore, advancements in transfer learning and data augmentation have improved artificial intelligence adoption in regions with limited datasets. While artificial intelligence technologies promise to boost agricultural productivity and sustainability, their

关键词： Artificial intelligence Nutrient management Soil moisture Crop protection Yield forecasting

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real-time Monitoring of College Sports Dance Competition Scenes Using deep learning Algorithms

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022年第0期2022卷

作者： Yang, Fei Wu, GeMuZi Shan, HongGang Namseoul Univ Grad Sch Cheonan South Korea Hebei Inst Phys Educ Dept Sports Art Shijiazhuang Hebei Peoples R China

In order to improve the real-time detection effect, therefore, a research on real-time scene detection of sports dance competition based on deep learning is proposed. The collected scene image is grayed by using the weighted average method, and the best image interpolation is calculated by using the deep learning method, so as to realize the smooth processing of sawtooth and mosaic information generated by panoramic mapping. After selecting the cube model, the processed scene information is projected to the visual plane to construct the panorama of the competition scene. Finally, combined with the three-frame difference, the changes between adjacent image frames are calculated to obtain the moving target. The test results show that the motion detection accuracy of professional dancers can reach more than 75.0% and that of amateur dancer can reach more than 64.2%.

关键词： deep learning

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YOLOv4-dense: A smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene

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IET image processing 2023年第2期17卷 570-580页

作者： Jiang, Yue Li, Wenjing Zhang, Jun Li, Fang Wu, Zhongcheng Chinese Acad Sci High Magnet Field Lab HFIPS Hefei 230031 Peoples R China Univ Sci & Technol China Hefei Peoples R China High Magnet Field Lab Anhui Prov Hefei Peoples R China

Edge-device-based object detection is crucial in many real-world applications, such as self-driving cars, ADAS, driver behavior analysis. Although deep learning (DL) has become the de-facto approach for object detection, the limited computing resources of embedded devices and the large model size of current DL-based methods increase the difficulty of real-time object detection on edge devices. To overcome these difficulties, in this work a novel YOLOv4-dense model is proposed to detect objects in an accurate, fast manner, which is built on top of the YOLOv4 framework but with substantial improvements. More specifically, lots of CSP layers are pruned since it will decrease inference speed. And to address the losing small objects problem, a dense block is introduced. In addition, a lightweight two-stream YOLO head is also designed to further reduce the computational complexity of the model. Experimental results on NVIDIA JETSON TX2 embedded platform demonstrate that YOLOv4-dense can achieve a higher accuracy, faster speed with smaller model size. For instance, on the KITTI dataset, YOLOv4-dense obtains 84.3% mAP and 22.6 FPS with only 20.3 M parameters, surpassing the state-of-the-art models with comparable parameter budget such as YOLOv3-tiny, YOLOv4-tiny, PP-YOLO-tiny by a large margin.

关键词： traffic engineering computing NVIDIA JETSON TX2 embedded platform feature extraction dense block YOLOv4-dense obtains edge-device-based object detection automobiles driver behavior analysis current DL-based methods smaller YOLOv4 Neural nets YOLOv4-tiny faster YOLOv4 Optical, image and video signal processing Computer vision and image processing techniques convolutional neural nets Traffic engineering computing object detection deep learning (artificial intelligence) smaller model size novel YOLOv4-dense model YOLOv4 framework edge devices YOLOv3-tiny embedded devices real-time object detection real-time edge-device computational complexity

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Improved RAD-YOLOv8s deep learning algorithm for personnel detection in deep mining workings of mines

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JOURNAL OF real-time image processing 2025年第3期22卷 1-14页

作者： Zhu, Yan song Wang, Xing Tang, Chao hong Liu, Cun yu Anhui Univ Sci & Technol Sch Mech & Elect Engn Huainan 232001 Peoples R China Hefei Comprehens Natl Sci Ctr Anhui Energy Lab Inst Energy Hefei 230000 Peoples R China

The real-time detection of personnel is a significant component of the construction of intelligent mines, especially for the personnel safety pre-warning in underground excavation workface. The underground excavation environment faces many critical challenges, including noise interference, uneven illumination, and the occlusion of mechanical equipment. Due to traditional detection algorithms' low detection accuracy and significant resource consumption, this study proposes an accurate and lightweight RAD-YOLOv8s (You Only Look Once v8) personnel detection algorithm for personnel safety pre-warning in underground excavation workface. First, a parameterized backbone network based on HGNetv2 (Hierarchical Graph Network) is employed to reduce the algorithm's complexity while collecting richer feature information. Second, according to the different targets, the C2f-AKConv (Alterable Kernel Convolution) module is introduced to the algorithm's neck network to flexibly adjust the size and shape of the convolution kernel, enhancing the algorithm's capacity to adapt to target deformation. Finally, a novel DCNV4-Dyhead (Deformable Convolutional Network v4 -Dynamic Head)module is developed to compensate for the limitations of classic standard convolution in long-range modeling and adaptive spatial aggregation to improve model detection performance. The proposed RAD-YOLOv8s detection algorithm was verified on a dataset of underground excavation workface people. The results showed that the mAP@0.5 and GFlops achieved 91% and 21.1%, which were 2.1% and 25.7% higher than YOLOv8s, respectively. Thus, the algorithm demonstrates improved accuracy and efficiency for real-time personnel safety monitoring in smart mines.

关键词： Personnel safety pre-warning Excavation workface RAD-YOLOv8s HGNetv2 AKConv

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PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2024年 208卷 136-157页

作者： Zhang, Ruinan Jin, Shichao Zhang, Yuanhao Zang, Jingrong Wang, Yu Li, Qing Sun, Zhuangzhuang Wang, Xiao Zhou, Qin Cai, Jian Xu, Shan Su, Yanjun Wu, Jin Jiang, Dong Nanjing Agr Univ Acad Adv Interdisciplinary Studies Plant Phen Res CtrCollaborat Innovat Ctr Modern C Coll AgrState Key Lab Crop Genet & Germplasm Enha Nanjing 210095 Peoples R China Chinese Acad Sci Inst Bot State Key Lab Vegetat & Environm Change Beijing 100093 Peoples R China Univ Hong Kong Inst Climate & Carbon Neutral Sch Biol Sci Pokfulam Rd Hong Kong Peoples R China

The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases with subtle organ changes, and they are not real-time, such as the vegetation index curve -based methods relying on entire growth stage data after the experiment was finished. Furthermore, it is key to improving the efficiency, scalability, and availability of phenological studies. This study proposes a two -stage deep learning framework called PhenoNet for the accurate, efficient, and real-time classification of key wheat phenophases. PhenoNet comprises a lightweight encoder module (PhenoViT) and a long short-term memory (LSTM) module. The performance of PhenoNet was assessed using a well -labeled, multi -variety, and large -volume dataset (WheatPheno). The results show that PhenoNet achieved an overall accuracy (OA) of 0.945, kappa coefficients (Kappa) of 0.928, and F1 -score (F1) of 0.941. Additionally, the network parameters (Params), number of operations measured by multiply -adds (MAdds), and graphics processing unit memory required for classification (Memory) were 0.889 million (M), 0.093 Giga times (G), and 8.0 Megabytes (MB), respectively. PhenoNet outperformed eleven state-of-the-art deep learning networks, achieving an average improvement of 3.7% in OA, 5.1% in Kappa, and 4.1% in F1, while reducing average Params, MAdds, and Memory by 78.4%, 85.0%, and 75.1%, respectively. The feature visualization and ablation analysis explained that PhenoNet mainly benefited from using time -series information and lightweight modules. Furthermore, PhenoNet can be effectively transferred across years, achieving a high OA of 0.981 using a two -stage transfer learning strat

关键词： Wheat phenology Dataset image classification deep learning Transfer learning Web application

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image Recognition Technology in Smart Agriculture: A Review of Current Applications Challenges and Future Prospects

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PROCESSES 2025年第5期13卷 1402-1402页

作者： Jiang, Chunxia Miao, Kangshu Hu, Zhichao Gu, Fengwei Yi, Kechuan Anhui Sci & Technol Univ Coll Intelligent Mfg Chuzhou 233100 Peoples R China Minist Agr & Rural Affairs Nanjing Inst Agr Mechanizat Nanjing 210014 Peoples R China

The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities.

关键词： image recognition technology smart agriculture deep learning sustainable development

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MCA-deeplabv3+: a cupping spot image segmentation network based on improved deeplabv3+

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SIGNAL image AND VIDEO processing 2025年第1期19卷 1-9页

作者： Ma, Lu-Yao Qin, Jian-Hua Liu, Ying-Bin Zeng, Gui-Fen Xu, Bao-Ling Huang, Ting-Ting Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Peoples R China Guilin Med Univ Affiliated Hosp 2 Guilin 541000 Peoples R China Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510006 Peoples R China

To monitor the condition of cupping spots in real-time during the operation of the automatic cupping machine, reduce the influence of the surrounding environment on the image, and improve the segmentation accuracy of the cupping spots, this paper proposes a network called MCA-deeplabv3+. Firstly, backbone network replaced by Mobilenetv2 to reduce the model size and improve feature extraction speed;Secondly, to further enhance the network's feature extraction capabilities, we added dilated convolution channels and integrated the CA attention mechanism into the ASPP module;Finally, data augmentation and brightness adjustment are performed on the dataset to improve the generalization of the model in different environments. The experimental results show that, in comparison with other segmentation models, MCA-deeplabv3+performs the best in cupping spot segmentation, with mIoU and mPA reaching 93.90% and 96.73%, respectively. The practicality and effectiveness of the cupping spot segmentation model presented in this paper are thoroughly demonstrated.

关键词： deeplabv3+ Cupping spot Semantic segmentation deep learning

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Garment image style transfer based on deep learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 3973-3986页

作者： Wang, Jing Hunan Int Econ Univ Sch Humanities & Arts Changsha Peoples R China

Before neural networks, image style transfer procedures had a common idea: analyze images with a certain style, build a mathematical or statistical model for the style, and then change the image to be transferred, so that it better fits the established model. In this paper, k-means and semantic closed natural matting algorithm is combined for image segmentation, the style and content in the image are extracted based on neural network, and the resulting image is synthesized by image reconstruction technology to realize the migration of national costume styles. Due to the serious artifacts of the output image, an improved image style transfer algorithm is adopted to constrain the transformation from the input image to the output image in the local affine transformation of the color space, and this constraint is expressed as a completely differentiable parameter term, image distortion is suppressed effectively. In the process of real photo style transfer, there is also space inconsistency. Smoothing is done to ensure that the space style is consistent after style processing, it greatly speeds up the operation speed. On NVIDIA GTX1080TI graphics card, algorithm is tested with 256x256 resolution images. It includes three indicators of average running time, memory usage and the number of styles generated by a single model, which are 0.06 s, 136.06MB and 1 respectively. These indicators can reflect the efficiency and flexibility of the algorithm.

关键词： Garment feature extraction deep learning closed form natural image matting algorithm K-means image segmentation image style transfer

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image category classification using 12-Layer deep convolutional neural network

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MULtimeDIA TOOLS AND APPLICATIONS 2023年第2期83卷 4017-4036页

作者： Veeramani, Vijayaraghavan Mohan, Laavanya Vignans Fdn Sci Technol & Res Deemed Univ Dept Elect & Commun Engn Guntur 522213 Andhra Pradesh India

In comparison to human vision, it's hard for systems to understand images and figure them out on their own. In the modern world, image processing is mostly done by convolutional neural networks that have been learned over time. As a result, our system categorizes real-time natural colour photographs using deep learning. In the majority of convolutional networks, the activation function is often a form of the rectified linear unit, which is prone to vanishing and exploding gradient problems. Though numerous studies have proposed solutions for resolving this issue, there has yet to be an efficient and feasible approach. To overcome this issue in our method, we combined Rectified Linear Unit (ReLU) variations without altering the activation functions or adding more layers. There are 12 hidden layers in the proposed network, and ten separate image categories were made from the CIFAR10 data set. In the experiment, we got 89.13 percent accuracy which is best when we compared our model to Alex net, Google net, and Resnet18. This shows that our model is better than convolutional neural networks with rectified linear units at categorizing natural pictures.

关键词： Convolutional neural network deep learning image classification Rectified linear unit

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