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Imaging Detection and Classification for Near-space Dim targets 7

Imaging Detection and Classification for Near-space Dim Targ...

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7th International conference on Vision, Image and signal processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

the motion mode of near-space targets is complex due to their high threat level. the target imaging faces low SNR and susceptibility to background noise. the existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. the algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. the improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. the outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © the Institution of Engineering & Technology 2023.

关键词： Extraction

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Localized Target Detection Algorithms in Low Illumination Wide-Field of View High-Resolution Environments 24

Localized Target Detection Algorithms in Low Illumination Wi...

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4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Xie, Chengyou Huang, Xiaohui Jia, Zhenhong Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

To solve the problem of difficult detection of small targets in wide-field videos captured by eagle-eye equipment in low-illumination environments. we propose a local moving target detection algorithm based on sample consistency. First, the initial background model is established using the first ten frames of the video. the contrast and texture features between the current and background frames are calculated, and the S component in the HSV space is extracted and fused to obtain the final features. Finally, the target area is located through the global segmentation map and the local tracking detection module is activated. Experimental results demonstrate that Compared to other algorithms, the proposed algorithm reduces the running time by implementing local detection. It effectively minimizes the interference caused by light sources and dark spot noise in low-light environments, thereby enhancing the accuracy of the algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

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A Ship Detection Model Based on YOLOX with Lightweight Adaptive Channel Feature Fusion and Sparse data Augmentation 18

A Ship Detection Model Based on YOLOX with Lightweight Adapt...

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18th IEEE International conference on Advanced Video and signal Based Surveillance (AVSS)

作者： Zhang, Quan Huang, Yanrong Song, Rui Shanghai Univ Sch Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665463829

Accurate and real-time detection of ships has become an essential part in maritime video surveillance and plays a vital role in national territorial water security. However, the existing ship detection models have poor recognition performance for some types of ships, such as small ships and ultra-long ships. Meanwhile, light and weather factors also affect the accuracy of the ship detection model, which blurs the boundaries between ships and the background. In this paper, a new ship detection model ShipYOLOX based on YOLOX is designed to solve those problems in existing models. Specifically, the model's ability to discriminate ships with complex contours is enhanced by adding a feature fusion module called Lightweight Adaptive Channel Feature Fusion (LACFF). Additionally, a new data augmentation algorithm Sparse Target Mosaic is designed to replace the original Mosaic. the new model ShipYOLOX has been validated in experiments with results showing that it can improve the accuracy of ship detection, which achieves an excellent performance of 87.4% AP75 on Seaships.

关键词： Location awareness Adaptation models Fuses Video surveillance Feature extraction data models Real-time systems

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RF Solder Ball Break Detection in Automotive FMCW Radars Using Isolated targets’ Phase

RF Solder Ball Break Detection in Automotive FMCW Radars Usi...

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Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： Esmaeil Kavousi Ghafi Oliver Lang Matthias Wagner Alexander Melzer Mario Huemer Institute of Signal Processing Johannes Kepler University Linz Austria Infineon Technologies Linz GmbH & Co. KG Austria

Embedded wafer level ball grid array (eWLB) packaging is a common technology for radar transceiver monolithic microwave integrated circuits (MMICs) in the automotive radar industry. One of the challenging issues with the eWLB packages is the radio frequency solder ball breaks (BBs), due to its adverse effects on the radar performance. In this paper, a method for BB detection (BBD) based on digital signal processing is proposed. this method exploits the collected data from the environment during the sensing task of the radar receiver to detect BBs in real-time. To the best of the authors’ knowledge, this work is the first one that addresses the BBD problem solely by digital signal processing, meaning that no additional analog hardware is required for the detection. To validate the performance of the proposed method, simulation results for measured radar data are provided.

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data Masking Analysis Based on Masked Autoencoders Architecture for Leaf Diseases Classification 1

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18th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2022

作者： Li, Li-Hua Tanone, Radius Department of Information Management Chaoyang University of Technology Taichung City Taiwan Fakultas Teknologi Informasi Universitas Kristen Satya Wacana Salatiga Indonesia

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Agriculture is one of the sectors that drives the economy of a country, including Indonesia. However, the prevention of plant diseases that can be classified through leaves must also be considered by farmers to produce good yields. In this study, we conducted an experiment by implementing self-supervised learning on plant disease recognition. We built the Masked Autoencoders model from scratch on the leaf diseases dataset (strawberry, potato, and tomato) to perform the downstream task, namely classification. In this experiment, we masked the input image with portions of 50%, 75% and 90% to analyse how the model works in reconstructing the missing pixels to resemble the original image. To see the performance of our experiment, metrics such as accuracy, SSIM and PSNR are used to see the effectiveness of the algorithm in image reconstruction. the results obtained in this experiment are that the more classes in the dataset, the downstream accuracy using the pre-trained MAE model is relatively small. On the other hand, a small number of classes will result in linear probing accuracy above 75% when classifying leaf disease recognition. For the SSIM and PSNR results, we identified that the image similarity results depended on the shape of the image where a more varied leaf shape would help the reconstruction process to resemble the original image. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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Aerial Photovoltaic Panel Infrared Image Defect Detection Method Based on Improved YOLOv8

Aerial Photovoltaic Panel Infrared Image Defect Detection Me...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Hongtao Hu Yunxiang Li Junfeng Wang Yong Lu School of Computer Science Xi'an ShiYou University Xi'an China No.3 Oil Production Plant PetroChina Changqing Oilfield Company Xi'an Shannxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Photovoltaic panels are the core equipment of photovoltaic power generation. Defects in photovoltaic panels are generally detected by analyzing infrared images taken by drones. However, the photovoltaic panel defects to be detected in infrared images are small, and traditional target detection algorithms are not sensitive to small targets. Misdetections and missed detections are prone to occur. In response to the above problems, this article proposes an Improved-YOLOv8 model to increase the accuracy of small target detection. Firstly, the CA attention mechanism is integrated into the YOLOv8 model to improve the ability to extract defect target features and locations; secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to replace Concat to reduce the loss of target information and enhance the model's ability to fuse targets of different scales. Maintain the sensitivity of the model to targets of different scales; finally, experiments were conducted using infrared image data of photovoltaic panels taken aerially from a photovoltaic power station. the results show that the accuracy and mAP@0.5 of the Improved-YOLOv8 model reached 88.5% and 95.2% respectively, effectively improving the YOLOv8 model's ability to handle small target defects in photovoltaic panels.

关键词： Photovoltaic systems Accuracy Attention mechanisms signal processing algorithms Object detection signal processing Feature extraction data models Drones Defect detection

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD)

Infrared small target detection based on long-short term fra...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Bing Zhu Jiahui Shi Ruixing Yu Weixin Gao Siyu Wang School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. the algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. the experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data.

关键词： Accuracy signal processing algorithms Object detection Cameras Motion compensation Trajectory Image sequences Motion measurement Time-domain analysis signal to noise ratio

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Underwater Multiple targets Tracking Using Multi-Bernoulli Filter and Improved data Association

Underwater Multiple Targets Tracking Using Multi-Bernoulli F...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Jiawei Gou Xionghou Liu Yixin Yang Chao Sun Ruijie Zhang Qifeng Bao Shaanxi Key Laboratory of Underwater Information Technology School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Xian China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Traditional multi-target tracking methods using Random Finite Set (RFS) theory mainly generate trajectories by using label RFS or performing data association after RFS filtering. For the case of target trajectories intersect, the too small distance between adjacent targets (i.e. targets are in close proximity) can cause label confusion or association errors, making it challenging for these methods to generate accurate trajectories. To solve this problem, we propose a target tracking method that integrates the multi-target multi-Bernoulli (MeMBer) filter and the improved data association. the MeMBer filter is used to obtain the target state estimations before data association. To avoid association errors when targets are in close proximity, we make the following improvements based on the nearest-neighbor data association method. When multiple targets are far from each other, we directly use the existing nearest-neighbor method to generate trajectories; when multiple targets are in close proximity, we treat the trajectory predictions of the last time step as the current trajectories, therefore avoiding data association errors. the simulation results demonstrate that the proposed method effectively solves the problem of inaccurate trajectory estimation during the intersection of target trajectories.

关键词： Radio frequency Target tracking Monte Carlo methods Simulation signal processing Information filters Filtering theory Trajectory State estimation Clutter

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Concept analysis of a frequency-sweeping delta/sigma beam-switching radar using machine learning

Concept analysis of a frequency-sweeping delta/sigma beam-sw...

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European Radar conference (EURAD)

作者： Mohammad Reza Seidi Goldar Jamshid Hassanpour Joachim Oberhammer School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (纸本)9781665447232

this paper investigates a novel radar concept that is based on a minimalistic, small-aperture antenna array but features intelligent beam-shape switching and artificial-intelligence signal processing. In contrast to conventional phased-arrays, size, cost, and hardware complexity are drastically reduced by the proposed dual-antenna array which can create a broad and a frequency-scanning notched beam shape. the angular-resolution and target discrimination performance of the proposed radar concept have been validated by radar simulations for single and multiple target scenarios. For the signal processing, two convolutional neural networks (CNN) and a multilayer perceptron model are benchmarked against each other. A further CNN is implemented for estimating the number of targets, which can be used to pre-select the type of network determining range and cross-range of multiple targets. this paper shows that a small antenna aperture frontend in combination with beam-shape switching and artificial-intelligence signal processing methods is a suitable hardware-efficient radar concept for accurate multi-target location.

关键词： Array signal processing Computational modeling Radar detection Radar Switches Benchmark testing Radar antennas

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Fast small Object Detection Algorithm Based on Feature Enhancement and Reconstruction 13

Fast Small Object Detection Algorithm Based on Feature Enhan...

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13th International conference on Wireless Communications and signal processing, WCSP 2021

作者： Huo, Zhiyong Yan, Tianwen Cao, Weiye Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. the paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. the algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4. © 2021 IEEE.

关键词： Convolution

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