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Hologram Upscaling for Viewing Angle Expansion Using Light Field Extrapolation with object detection algorithm

CURRENT OPTICS AND PHOTONICS 2025年第1期9卷 55-64页

作者： Shin, Dong-Ha Song, Chee-Hyeok Lee, Seung-Yeol Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

As demand for high-resolution holographic displays in augmented and virtual reality (AR/VR) increases, the limitations of traditional computer-generated holography (CGH) upscaling methods, including bicubic interpolation and deep learning-based techniques, become apparent. These methods predominantly estimate additional pixels without considering the reduction of pixel pitch, inherently constraining their capacity to effectively expand the viewing angle. Our study introduces a novel approach for viewing angle expansion through light field (LF) extrapolation by applying an object detection algorithm. This process starts by analyzing the object position and depth information of each LF view extracted from CGH patterns with the object detection algorithm. The use of these data allows us to extrapolate LF views beyond their initial viewing angle limit. Subsequently, these expanded LF views are resynthesized into the CGH format to expand the viewing angle. With our approach, the viewing angle was successfully doubled from an initial 3.54 degrees to 7.09 degrees by upscaling a 2K 7.2 mu m CGH to a 4K 3.6 mu m CGH, which was verified with both numerical simulation and optical experiments.

关键词： Computer-generated holography Digital holography upscaling Light field object detection algorithm Viewing angle

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A Temperature Monitoring Method For Power Electronic Converter Based on Infrared Image and object detection algorithm

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第1期59卷 1090-1099页

作者： Yang, Hongcheng Chen, Yu Shang, Yi Yu, Changqi Kang, Yong Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

Power electronic converters are more and more widely used, and abnormal temperature of converter components is the most important factor in converter failure. To improve the reliability of the converter design, it is necessary to monitor the temperature of key components in the converter during the prototype test stage. The temperature measurement method of infrared thermal images has rich temperature information, wide detection range, and does not affect the original circuit design. However, in the current automatic temperature measurement methods, it is necessary to manually establish a standard matching template for the infrared thermal image of the circuit to be tested, which indicates a large workload and poor versatility. This paper proposes a method for fully automatic temperature monitoring of converter components. This method is based on a deep learning object detection algorithm, which can automatically identify the type of converter components, obtain partial infrared thermal images of components through heterogeneous image registration, achieve accurate component temperature monitoring and facilitate the converter state monitoring and fault detection. The advantages of this method are: 1) there is no need to manually establish a standard template for each converter;2) it can monitor the converter temperature without manual intervention;3) combining the temperature information and circuit prior knowledge, the state monitoring and fault diagnosis can further be realized. The experimental results also verify the feasibility and accuracy of this method.

关键词： Temperature measurement Temperature sensors Monitoring Power electronics Standards Temperature distribution Training Image registration infrared thermal image object detection algorithm power electronic converter

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A Method for Measuring Respiratory Rate based on an object detection algorithm

A Method for Measuring Respiratory Rate based on an Object D...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Imaizumi, Nanami Ohyama, Futoshi Matsumoto, Hiroki Systems Life Engineering Maebashi Institute of Technology Maebashi Japan Faculty of Nursing Tokai University Tokyo Japan

ISBN: (纸本)9798350389210

Measuring the respiratory rate of inpatients is important for detecting physical changes and signs of abnormality in medical field. Therefore, respiratory rate of inpatients is regularly measured. However, measuring respiratory rates places a heavy burden of healthcare workers. The reason is that respiratory rate is measured by visual observation. To reduce the burden of healthcare workers, we investigate a simple, non-invasive and automatic method of measuring respiratory rate. We focus on object detection algorithms. object detection algorithms detect objects in the image. Then, bounding boxes are output at the locations of detected objects. We propose a method for measuring respiratory rate using the width of the bounding box. © 2024 IEEE.

关键词： Biometrics Non-contact object detection algorithm Respiration YOLOv8

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Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm

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ADVANCES IN COLLOID AND INTERFACE SCIENCE 2022年 304卷 102663页

作者： Huang, Zongyu Ni, Yang Yu, Qun Li, Jinwei Fan, Liuping Eskin, N. A. Michael Jiangnan Univ Sch Food Sci & Technol 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Univ Manitoba Dept Food & Human Nutr Sci Winnipeg MB R3T 2N Canada

Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.

关键词： Emulsion Deep learning Food microstructure Pickering emulsion object detection algorithm Characterization Morphology

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object detection-Tracking algorithm for Unmanned Surface Vehicles Based on a Radar-Photoelectric System

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IEEE ACCESS 2021年 9卷 57529-57541页

作者： Yu, Qingze Wang, Bo Su, Yumin Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin 150001 Peoples R China

object tracking is an important basis for the autonomous navigation of unmanned surface vehicles. However, several problems still must be addressed for a wide applicating of object tracking in unmanned surface vehicles. First, if multiple objects of the same classification exist in the same field of view, then stable extraction of an object is difficult. Second, in an environment with a complex background and large changes in object shape, the tracking accuracy is low, and object tracking errors and tracking loss can easily occur. Third, much time is required to detect a high-resolution real-time video stream, not meeting the delay requirement of the photoelectric servo stable tracking. To resolve these problems, this paper proposes an object detection-tracking algorithm based on a radar-photoelectric system. The algorithm combines an object detection algorithm with an object tracking algorithm and involves the following steps. First, a first-frame object extraction algorithm is used to extract the tracking object from the first frame. Second, a region of interest (ROI)-prediction algorithm is used to predict ROIs and detect objects in these ROIs. This algorithm can effectively solve the above problems in marine tests. When multiple objects of the same classification exist in the same field of view, the algorithm can extract the radar-guided object stably. When faced with a complex background and a large change in object shape, the algorithm substantially improves the accuracy and robustness of object tracking. Compared with the conventional object detection algorithm, the time consumption of this algorithm is reduced by 25.8%.

关键词： Radar tracking object tracking Prediction algorithms Correlation Filtering algorithms Classification algorithms Marine vehicles Unmanned surface vehicle radar-photoelectric system first-frame object extraction algorithm ROI-prediction algorithm object detection algorithm object tracking algorithm

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A dynamic object filtering approach based on object detection and geometric constraint between frames

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IET IMAGE PROCESSING 2022年第6期16卷 1636-1647页

作者： Wei, Jiansheng Pan, Shuguo Gao, Wang Zhao, Tao Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China

In order to eliminate the influence of moving targets in visual positioning, a dynamic object filtering approach based on object detection and inter-frame geometric constraints is proposed to filter the dynamic objects in monocular images. The object detection algorithm is firstly used to identify and locate objects in the single-frame image, and the object matching between frames is performed. Then, the depth estimation network and pose recovery network are trained jointly to output estimated depth along with transformation matrix of object centroids between frames respectively. Finally, the mapping centroid is obtained from the estimated depth and inter-frame transformation matrix. The joint constraint function is performed to complete the detection and filtering of dynamic objects. The experimental results show that the proposed dynamic object filtering approach not only can filter moving objects in sequence images accurately but also allows to reserve the dynamic objects that are temporarily in the stop state. The generalization ability of this approach is also verified in a real urban road scene and meets the requirements of follow-up research in visual positioning.

关键词： image sequences object detection object detection algorithm image matching dynamic object filtering approach single-frame image traffic engineering computing image motion analysis inter-frame geometric constraints object centroids

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A review of vehicle detection methods based on computer vision

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Journal of Intelligent and Connected Vehicles 2024年第1期7卷 1-18页

作者： Changxi Ma Fansong Xue School of Traffic and Transportation Lanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control Lanzhou 730070China

With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation *** order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant *** study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection *** the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network ***,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation *** to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and *** single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection *** the follow-up research,it is important to consider the balance between detection efficiency and detection ***,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into *** will ensure better application of the research findings in engineering practice.

关键词： intelligent transportation system computer vision deep learning vehicle detection object detection algorithm

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Method for detecting dead caged laying ducks based on infrared thermal imaging

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International Journal of Agricultural and Biological Engineering 2024年第6期17卷 101-110页

作者： Yu Yan Qiaohua Wang Weiguo Lin Shucai Wang Yue Gu Yifan Heng College of Engineering Huazhong Agricultural UniversityWuhan 430070China

To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweight object detection algorithm is developed,utilizing YOLO v8n as the baseline *** backbone network is replaced with StarNet,which is based on“Star Operate”.Additionally,the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module,and it is inserted into the Neck structure of the baseline *** module L-SPPF replaces the SPPF module in the baseline model to enhance feature ***,a lightweight shared convolutional detection head,termed SCSB-Head,is introduced to reduce computational *** improvements collectively form a lightweight object detection algorithm named *** results show that SLSS-YOLO achieves mAP@50%-95%,precision,and recall scores of 80.50%,99.44%,and 98.46%,*** to the baseline model,these metrics improve by 1%,1.98%,and 0.26%,*** terms of model size and detection speed,SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs,achieving an FPS rate of 134.9 f/*** represents a reduction of 52.16%and 43.90%in parameters and FLOPs,respectively,while increasing FPS by 5.4 f/s compared to the baseline ***,an object tracking model is constructed using SLSS-YOLO and *** tests demonstrate that Hybrid-SORT achieves zero ID-Switches,with a detection speed of 10.9 ms/*** outperforms Bot-SORT,ByteTrack,Deep OC-SORT,and OC-SORT in terms of tracking ***,the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments,providing a reference for automated inspection in caged duck farms.

关键词： caged laying duck object detection algorithm YOLO infrared thermal imaging dead poultry

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Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

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Computers, Materials & Continua 2023年第5期75卷 3027-3044页

作者： Youngmin Kim Sunwoo Hwang Jaemin Park Joouk Kim Department of Systems Engineering Ajou UniversitySuwon16499Korea Advanced Logistics System Research Department Korea Railroad Research InstituteUiwang16105Korea

With the growth of the online market,demand for logistics and courier cargo is increasing ***,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly *** joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery ***,several courier companies use different types of courier *** a system has a problem of information data transmission ***,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for *** addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the *** results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the *** it can be used as a basis for securing the operational reliability of the joint courier logistics system.

关键词： Joint courier logistics base infrastructure logistics cooperation urban public infrastructure YOLOv4 object detection algorithm

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High-precision real-time UAV target recognition based on improved YOLOv4

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COMPUTER COMMUNICATIONS 2023年第1期206卷 124-132页

作者： Dong, Yuxing Ma, Yujie Li, Yan Li, Zhen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun Peoples R China Shanghai Electromech Engn Inst Shanghai 201109 Peoples R China

In recent years, unmanned aerial vehicles (UAVs) have gained widespread use in both military and civilian fields with the advancement of aviation technology and improved communication capabilities. However, the phenomenon of unauthorized UAV flights, or "black flying", poses a serious threat to the safe flight of aircraft in airspace and public safety. To effectively interfere with and attack UAV targets, it is crucial to enhance the detection and identification of "low, slow and small"UAVs. This study focuses on achieving high-precision and lightweight detection and identification of four-rotor, six-rotor, and fixed-wing UAVs in low-altitude complex environments. By combining deep learning target detection with superresolution feature enhancement, a lightweight UAV detection model is designed and field-tested for verification. To address the challenge of detecting small UAV targets with limited information, the feature fusion network is enhanced based on the traditional YOLOv4 algorithm to improve the detection ability of small targets via small target enhancement and candidate box adjustment. The feasibility of the improved network is quantitatively and qualitatively analyzed. Channel pruning and layer pruning are then applied to the network, significantly reducing its depth and width and realizing a lightweight network. Finally, reasoning quantification is conducted on the embedded platform to enable end-side deployment of the target detection algorithm.

关键词： UAV Photoelectric detection system Superresolution reconstruction object detection algorithm

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