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2021 2nd International Conference on Signal processing and Computer Science, SPCS 2021

作者： Wang, Chen xi Chen, xin Zou, Han Yu He, Song Tang, xiao Air Force Early Warning Academy Hu Bei Wu Han430014 China

All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Millimeter waves

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Blind CT image Quality Assessment Model Based on CT image Statistics 2020

Blind CT Image Quality Assessment Model Based on CT Image St...

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The Fourth International Symposium on image Computing and Digital Medicine

作者： Jiayu Duan Jianmei Cai Shaohua Zhi Xuanqin Mou Xi'an Jiaotong University China

ISBN: (纸本)9781450389686

image quality assessment is widely used in many image processing tasks, which can help researchers adjust image processing algorithms, design imaging systems, and evaluate image processing systems. Generally, CT image quality assessment can be categorized into task-specific and general image quality evaluation. Task-specific image quality assessment evaluates the performance of the imaging system or the detectability of the tumor. These IQA index, for example, are modulation transfer function (MTF), Signal-to-Noise Ratio (SNR), observer model, etc. General image quality assessment measures the general reconstruction image quality under different reconstruction algorithms. SSIM (Structural Similarity), Mean Squared Error (MSE), etc. are the traditional general image quality assessment indexes widely used in nowadays CT image quality assessment. The drawback of these indexes is the demand for reference images, which is not practical in the real CT system. In this paper, we design a CT image dataset, and by using this dataset, and we propose a blind image quality assessment (BIQA) model based on CT image statistics, which can be employed to measure the algorithms under no reference image situation. Different from other image datasets, we recruited no-converged images of the reconstruction process in designing datasets, which enables our BIQA model to evaluate non-converged images during the iterations. Hence, the BIQA model can be embedded in the reconstruction process to monitor reconstructed image quality during iterations.

关键词： CT image dataset CT image statistics blind image quality assessment

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基于FPGA的量子点宽光谱图像盲元补偿和锐化算法实现

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光学与光电技术 2025年第1期23卷 26-34页

作者：刘泉刘欢杨鹏西安工业大学光电工程学院陕西西安710021

量子点光电探测器是目前研究的热点,由于使用了特殊的感光材料,其具有更宽的光谱响应范围(350~2000 nm)。然而,在成像芯片制备过程中,因为合成工艺不成熟和像元移植率低等因素影响,得到的图像会存在盲闪元扎堆和细节模糊的图像问题。因... 详细信息

量子点光电探测器是目前研究的热点,由于使用了特殊的感光材料,其具有更宽的光谱响应范围(350~2000 nm)。然而,在成像芯片制备过程中,因为合成工艺不成熟和像元移植率低等因素影响,得到的图像会存在盲闪元扎堆和细节模糊的图像问题。因此,为提高图像的质量,减弱图像中的盲闪元噪声和细节模糊带来的影响,提出用自适应中值滤波算法和反锐化掩膜算法对宽光谱图像进行增强,并借助FPGA(Field Programmable Gate Array,FPGA)流水线处理的优势,在xilinx A7系列硬件上进行部署。结果表明,当输入一帧分辨率为640×512的图像时,处理的图像峰值信噪比能增大17%。在50 MHz的时钟下,处理过程耗时约6.16 ms,实时性强,硬件占用率低,对宽光谱成像系统的优化具有实际意义。

关键词：量子点宽光谱图像现场可编程门阵列自适应中值滤波反锐化掩膜

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Autostereoscopic Measurement System for Rapid 3D Inspection of Wire Bonding

Autostereoscopic Measurement System for Rapid 3D Inspection ...

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2021 International Conference of Optical Imaging and Measurement, ICOIM 2021

作者： Gao, Sanshan Cheung, Chi Fai Li, Da The Hong Kong Polytechnic University State Key Laboratory of Ultra-Precision Machining Technology Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9780738112121

The quality of wire bonding is an important factor in the manufacturing of various precision electronic components in semiconductor industry. The wire bonding defects, including high loop and low loop, determined by the bonding height information, inevitably affect the performance of the product. It is vital to measure height information quickly and accurately to reduce the failure rate in the manufacturing process. However, traditional measurement system often needs to move the target samples for a number of times. To address the key measurement problems, a three-dimensional (3D) optical measurement system is developed based on autostereoscopy technologies. A micro-lens array is used to divide the light emitted by the sample into different directions. The elemental images from different view angles contain original 3D information and is recorded by a high-resolution image sensor. On the basis of the multi-view stereo information contained in the image, the elemental images can be refocused into multi-focus images. The 3D information can be directly extracted from the multi-focus images through self-developed algorithms. Experiment results show that the system can complete the measurement in 2 seconds and reach a high measurement accuracy at a sub-micrometer level. This verifies the feasibility of the proposed measurement system which is efficient and accurate for 3D inspection of wire bonding. © 2021 IEEE.

关键词： Stereo image processing

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基于视觉感知的表面缺陷智能检测理论及工业应用

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计算机集成制造系统 2022年第7期28卷 1996-2004页

作者：柴利任磊顾锞陈佳鑫黄博叶琦曹玮浙江大学控制科学与工程学院浙江杭州310027 北京航空航天大学自动化科学与电气工程学院北京100191 北京工业大学信息学部北京100124 北京航空航天大学计算机学院北京100191 西北工业大学机电学院陕西西安710072 上海市航空发动机数字孪生重点实验室上海200241 中国航发商用航空发动机有限责任公司上海200241

由于在工业产品质量监控中不可比拟的优势,基于视觉感知的表面缺陷检测近年来得到了很多研究者的持续关注,且已广泛应用于不同工业领域,包括汽车工业、半导体加工、玻璃制造、钢铁冶金等。AI学习算法与视觉传感技术的飞速发展为表面缺... 详细信息

由于在工业产品质量监控中不可比拟的优势,基于视觉感知的表面缺陷检测近年来得到了很多研究者的持续关注,且已广泛应用于不同工业领域,包括汽车工业、半导体加工、玻璃制造、钢铁冶金等。AI学习算法与视觉传感技术的飞速发展为表面缺陷检测研究带来了新的机遇与挑战。综述了基于视觉感知的表面检测研究中的主要方法与进展,重点介绍了图像处理、几何深度学习、面向目标检测的深度学习等方向的研究现状,这些研究有望为表面缺陷智能检测技术的发展带来突破。讨论了工业图像检测与识别在钢铁冶金、大气污染监测以及航空发动机缺陷检测3个领域的应用。最后,提出了值得研究的挑战性问题。

关键词：视觉感知智能检测深度学习图像处理表面缺陷检测小样本目标检测人工智能

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Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention 11

Multi-scale oriented object detection in aerial images based...

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SPIE 11th International Symposium on Multispectral image processing and Pattern Recognition (MIPPR) - Remote Sensing image processing, Geographic Information systems, and Other Applications

作者： Fei, Jingjing Wang, Zhicheng Yu, Zhaohui Gu, xi Wei, Gang Tongji Univ Res Ctr CAD Shanghai Peoples R China

ISBN: (纸本)9781510636422

Object detection is a fundamental yet challenging problem in natural scenes and aerial scenes. Although region based deep convolutional neural networks (CNNs) have brought impressive improvements for object detection in natural scenes, detecting oriented objects in aerial images still remains challenging, due to the complexity of the aerial image backgrounds and the large degree of freedom in scale, orientation, and density. To tackle these problems, we propose a novel network, composed of backbone structure with global attention module, multi-scale object proposal network and final oriented object detector, which can efficiently detect small objects, arbitrary direction objects, and dense objects in aerial images. We utilize pyramid pooling blocks as a global attention module on the top of the backbone structure to generate discriminative feature representations, which provide diverse context information and complementary receptive field for the detector. The global attention module can help the model reduce false alarms and incorrect classifications in the complex aerial image backgrounds. The multi-scale object proposal network aims to generate object-like regions at different scales through several intermediate layers. After that, these regions are sent to the detector for refined classification and regression, which can alleviate the problem of variant scales in aerial images. The oriented object detector is designed to generate predictions for inclined box. The quantitative comparison results on the challenging DOTA dataset show that our proposed method is more accurate than baseline algorithms and is effective for objection detection in aerial images. The results demonstrate that the proposed method significantly improves the performance.

关键词： Object detection aerial image convolution neural networks attention mechanism

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SoftPatch: unsupervised anomaly detection with noisy data 22

SoftPatch: unsupervised anomaly detection with noisy data

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Proceedings of the 36th International Conference on Neural Information processing systems

作者： xi Jiang Jianlin Liu Jinbao Wang Qian Nie Kai Wu Yong Liu Chengjie Wang Feng Zheng Southern University of Science and Technology Department of Computer Science and Engineering Tencent Youtu Lab

ISBN: (纸本)9781713871088

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

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VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence

arXiv

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arxiv 2022年

作者： Cai, Yuwei Li, Huanlin Hong, Juncao Xu, Peng Cheng, Hui Zhu, xiaomin Hu, Bingliang Hao, Zhifeng Fan, Zhun Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering University of Shantou Guangdong China School of Computer Science and Engineering University of Sun Yat-Sen Guangdong China College of Systems Engineering National University of Defense Technology Hunan Changsha China Xi’an Institute of Optics and Precision Mechanics Shanxi China

Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

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基于模糊规则的插值推理算法综述

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计算机学报 2022年第8期45卷 1687-1711页

作者：李方轶李映杨静西北工业大学计算机学院西安710129 空天地一体化大数据技术及应用国家工程实验室西安710129 陕西省语音与图像信息处理重点实验室西安710129

基于模糊规则的近似推理系统是在模糊集和模糊逻辑理论上建立的,在数学、工程、计算机等科学领域得到了迅速的发展.它作为解决建模和推理中不精确和模糊问题的有效工具,利用模糊if-then规则完成推理任务.传统推理机制只能使用稠密的规... 详细信息

基于模糊规则的近似推理系统是在模糊集和模糊逻辑理论上建立的,在数学、工程、计算机等科学领域得到了迅速的发展.它作为解决建模和推理中不精确和模糊问题的有效工具,利用模糊if-then规则完成推理任务.传统推理机制只能使用稠密的规则库进行推理,其能获得有效结论的前提是保证任何输入的观测值都能与现有的模糊规则相匹配.模糊规则插值(Fuzzy Rule Interpolation,FRI)技术能够有效地利用不完备(或稀疏)的规则库实现推理,为没有规则匹配的观测值通过规则插值估计出其结论.本文系统地综述了基于模糊规则的插值推理技术,着重介绍基于α-截集和基于中间规则的两大类插值方法,通过代表性算法揭示了这两类方法的基本思想.同时,还简要介绍了模糊插值推理系统的其他方法和规则插值算法的实际应用.此外,本文汇总了11个常用的评价指标,并对典型算法进行了系统的比较和讨论.最后对未来的研究工作进行了展望.

关键词：模糊规则插值不完备规则库模糊α-截集模糊中间规则模糊插值推理

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A novel image recognition approach using multiscale saliency model and GoogLeNet 18

A novel image recognition approach using multiscale saliency...

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18th image processing: algorithms and systems Conference, IPAS 2020

作者： Yang, Guoan Jian, Libo Lu, Zhengzhi Yang, Junjie Liu, Deyang School of Electronic and Information Engineering Xi’an Jiaotong University No.28 Xianning West Road Xi’an CityShaanxi Province710049 China

It is very good to apply the saliency model in the visual selective attention mechanism to the preprocessing process of image recognition. However, the mechanism of visual perception is still unclear, so this visual saliency model is not ideal. To this end, this paper proposes a novel image recognition approach using multiscale saliency model and GoogLeNet. First, a multi-scale convolutional neural network was taken advantage of constructing multiscale salient maps, which could be used as filters. Second, an original image was combined with the salient maps to generate the filtered image, which highlighted the salient regions and suppressed the background in the image. Third, the image recognition task was implemented by adopting the classical GoogLeNet model. In this paper, many experiments were completed by comparing four commonly used evaluation indicators on the standard image database MSRA10K. The experimental results show that the recognition results of the test images based on the proposed method are superior to some state-of-the-art image recognition methods, and are also more approximate to the results of human eye observation. © 2020, Society for Imaging Science and Technology

关键词： image recognition

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