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Heterogeneous face detection based on multi-task cascaded convolutional neural network

IET IMAGE PROCESSING 2022年第1期16卷 207-215页

作者： Yang, XianBen Zhang, Wei Beihua Univ Coll Comp Sci & Technol Jilin 13200 Jilin Peoples R China

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.

关键词： O-Net regression analysis fully convolutional attention networks P-Net reduced network CASIA NIR-VIS 2.0 heterogeneous face feature detection candidate window frame plus classifier convolutional neural nets deep learning R-Net image representation output network face images feature extraction heterogeneous face detection deep learning (artificial intelligence) face recognition MTCNN multi-task cascaded convolutional neural network CUFSF computer vision boundary regression CUFS near-infrared sketch face recognition scenes object detection face detection technology nonmaximum suppression image classification image pyramid facial target detection

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Research on the application of infrared weak and small target detection algorithm based on SVM posterior probability in weekly vision search system

Research on the application of infrared weak and small targe...

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Conference on Optical Sensing and Imaging Technologies and Applications / International Symposium on Optoelectronic technology and Application (OTA) / Annual Conference of the Chinese-Society-for-Optical-Engineering (CSOE)

作者： He, Zhuo Zhang, Jinsuo Chen, Xu Nan, Tianzhang Tianjin Jinhang Inst Tech Phys 58 Zhonghuan Xi Rd Tianjin 300381 Peoples R China

ISBN: (纸本)9781510623354

infrared weak and small target detection has important application value in military field, and is a hot research topic in the field of target detection. With the research and development of technology, some guiding and innovative detection algorithms are emerging, especially the advantage of machine learning algorithm. In this paper, the infrared week and small target detection is considered as the two classification problem of target and background, and an infrared week and small target detection algorithm based on multiple features SVM posterior probability is proposed and applied to the weekly vision search system. In the experiment, the SVM classification model is obtained by using 8 good segmentation characteristics as the main reference value of the identification target and background, and training the training set through a large number of target and background samples. Finally, the SVM posterior probability is selected as the output of the detection result. The simulation experiment of this algorithm and the application experiment in the transplant week search system show that the method of this method has higher target detection probability (not less than 95%), the false alarm probability is lower, and the time complexity of the algorithm is low, and the hardware resource is less, and it has a good prospect of engineering application.

关键词： SVM posteriori probabilities weekly vision search system multiple features

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Intensity correlation techniques for passive optical device detection

Intensity correlation techniques for passive optical device ...

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infrared technology for target detection and classification 1981

作者： Gustafson, Steven C. University of Dayton Research Institute 300 College Park Avenue DaytonOH45469 United States

The use of intensity correlation techniques for the detection of passive optical devices is considered. It is assumed that the devices have small angular sizes due to their large distances from the detection system, and it is assumed that the final optical apertures of the devices appear to be stationary, whereas their surroundings appear to fluctuate. This difference in apparent motion forms the basis for intensity correlation detection, which has potential advantages because of its insensitivity to phase variations due, for example, to motion of the detection system or to atmospheric turbulence. Some relationships between intensity correlation detection and certain areas in optical physics, including laser radar (lidar), intensity correlation interferometry, and speckle phenomena due to moving diffuse objects are discussed. A simple model of intensity correlation detection is developed and related to the influence of stationary coherent background illumination on the autocorrelation function in time of a coherently illuminated moving rough surface. An expression for this autocorrelation function is derived and discussed for certain elementary conditions. © 1982 SPIE. All rights reserved.

关键词： Optical radar

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High anti-interference and FPGA-oriented method for real-time ship detection based on structured LBP features

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JOURNAL OF SUPERCOMPUTING 2022年第11期78卷 13780-13813页

作者： Miao, Rui Jiang, Hongxu Tian, Fangzheng Li, Xiaobin Beihang Univ Sch Comp Sci & Engn Beijing Key Lab Digital Media Beijing 100191 Peoples R China

With the continuous enhancement of remote sensing technology, using satellite to detect and identify targets has important research significance in military and civil fields. Due to the influence of many interference factors, the background of large-scale infrared remote sensing images is often complex and the target intensity is weak, and efficient and accurate target detection has become a difficult issue. Therefore, the purpose of this paper is to solve the issues of inaccurate target detection and inefficient hardware deployment. Firstly, the rough detection method based on the grey variance and gradient features can effectively extracted the target candidate region and eliminate simple background interference. Secondly, the fine detection method based on multiple features is used for ship classification, in which structured LBP (local binary pattern) features is applied to calculate the structured feature histogram, screen the local target features, and calculate and classify the global features through two-dimensional Fourier features. Thirdly, the improved non-maximum suppression method is used to merge adjacent target points to ensure the accuracy of the algorithm to the greatest extent. Finally, an efficient feature computing unit and multichannel storage structure is proposed to optimize the FPGA (field-programmable gate array) implementation and relieve the pressure of high data rate processing and can eliminate a lot of computational redundancy. Experimental results validate that, compared with some advanced methods, our method can reduce the processing time by up to 1.71 times while the ship detection performance can be improved by up to 5.4%. Furthermore, the hardware processing capability was verified by three indicators, among which the traversal processing performance and throughput can increase by up to 1.7 and 6.7%, while the delays of key modules can decrease by up to 4%.

关键词： Ship detection Structured LBP features Anti-interference FPGA-oriented Real time

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infrared dim target detection via hand-crafted features and deep information combination 17

Infrared dim target detection via hand-crafted features and ...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Cao, Siying Lian, Ruiyi Zhang, Yiwei Wu, Fengyi Peng, Zhenming Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781665409841

infrared target detection is the key technology of infrared search and tracking system. In the case of complex background, dim target detection face difficulties such as high algorithm complexity and low robustness. To solve these problems, an infrared dim target detection method via handcrafted features and deep information combination is proposed. Firstly, an adaptive Top-Hat segmentation is used to extract candidate target region, then 128-dimensional deep learning features and 106-dimensional hand-crafted features are calculated for candidate region respectively. Finally, features are concatenated and input into a 4-layer fully connected network for feature fusion and classification to exclude false alarm sources. Experiments show that the proposed method can detect infrared dim targets accurately and robustly in various scenes.

关键词： infrared target detection dim target feature fusion

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infrared target array development

Infrared target array development

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infrared technology for target detection and classification 1981

作者： McIntire, Thomas O. Scott, Edward A. Methodology and Instrumentation Division U.S. Army Yuma Proving Ground STEYP-MMI YumaAZ85364 United States

A "life size" thermal target array has been developed to facilitate in-flight testing of airborne weapon systems containing night vision subsystems. This in-flight testing to measure the performance of the night vision subsystem and its effect on overall weapon system performance is essential to the test and evaluation process of the particular weapon under test. This measurement of subsystem performance is called the Modulation Transfer Function, or MTF. In addition, a laser designator subsystem is frequently incorporated in a precision guided munition weapon system. In the test and evaluation of the designator, such quantities as beam quality (energy distribution), beam divergence, and beam wander are of interest. The thermal targets may be used to evaluate armored weapon systems. The capability of providing carefully controlled and variable thermal signatures in a field test environment is considered unique. The thermal target array consists of three targets: A six bar recognition target, a two bar detection target, and a laser designator scoring board (cross-hair). The image dimensions of 2.3 meters by 2.3 meters were derived from an optimized threat envelope. The thermal signatures of the targets are controllable to within 0.3 C about a differential setpoint. This differential setpoint is measured between the active element and the target background (or "ambient"). Several differential temperature settings are available to the test officer: 1.25°C, 3°C, 5°C, 7.5°C, and 10°C. This paper reviews the thermal array test objectives, target array fabrication, methodology of target utilization, and representative results. © 1982 SPIE. All rights reserved.

关键词： Vision

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Efficient multi-level cross-modal fusion and detection network for infrared and visible image

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ALEXANDRIA ENGINEERING JOURNAL 2024年 108卷 306-318页

作者： Gao, Hongwei Wang, Yutong Sun, Jian Jiang, Yueqiu Gai, Yonggang Yu, Jiahui Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang 110159 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Zhejiang Univ Dept Biomed Engn Hangzhou 310027 Peoples R China Binjiang Inst Zhejiang Univ Innovat Ctr Smart Med Technol & Devices Hangzhou 310053 Peoples R China

With the rapid development of uncrewed aerial vehicle (UAV) technology, detecting aerial images has found significant applications across various domains. However, existing algorithms overlook the impact of illumination on target detection, resulting in less satisfactory detection performance under low-light conditions. We propose EfficientFuseDet, a visible and infrared image fusion detection network to overcome this issue. First, an effective multilevel cross-modal fusion network called EfficientFuse is presented to combine complementary information from both modalities better. EfficientFuse captures local dependencies and global contextual information in shallow and deep layers, seamlessly combining complimentary local and global features throughout the network. The generated fused images can exhibit clear target contours and abundant texture information. Second, we propose a detection network called AFI-YOLO, which employs an inverted residual vision transformer backbone (IRViT) to effectively address the challenges associated with background interference in fused images. We design an efficient feature pyramid network (EFPN) that efficiently integrates multiscale information, enhancing multiscale detection capability using aerial images. A reparameterization decoupling head (RepHead) is proposed to further improve target classification and localization precision. Finally, experiments on the DroneVehicle dataset indicate that the detection accuracy using fused images can reach 47.2%, which is higher than that observed with visible light images of 45 %. Compared to state-of-the-art detection algorithms, EfficientFuseDet exhibits a slight decrease in speed. However, it demonstrates superior detection capabilities and effectively enhances the detection accuracy using aerial images under low-light conditions.

关键词： Uncrewed aerial vehicles Aerial image Image fusion Object detection

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detection and recognition of UA targets with multiple sensors

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AERONAUTICAL JOURNAL 2023年第1308期127卷 167-192页

作者： Chen, W. S. Chen, X. L. Liu, J. Wang, Q. B. Lu, X. F. Huang, Y. F. China Acad Civil Aviat Sci & Technol Beijing 100028 Peoples R China Naval Aviat Univ Yantai 264001 Peoples R China Beihang Univ Beijing 100191 Peoples R China

Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

关键词： UA detection Deep learning Multi-sensor Information fusion

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Automatic target recognition and detection in infrared imagery under cluttered background 3

Automatic target recognition and detection in infrared image...

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Congress on target and Background Signatures III

作者： Gundogdu, Erhan Koc, Aykut Alatan, A. Aydin ASELSAN Res Ctr Intelligent Data Analyt Res Prog Dept Ankara Turkey Middle East Tech Univ Elect & Elect Engn Dept Ankara Turkey Middle East Tech Univ Ctr Image Anal OGAM Ankara Turkey

ISBN: (纸本)9781510613294;9781510613287

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

关键词： autoencoders neural networks infrared object recognition

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Obstacle detection Method of Underground Electric Locomotive Rail Based on Instance Segmentation

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TRANSPORTATION RESEARCH RECORD 2024年第6期2678卷 708-723页

作者： Tong, Jiale Wang, Shuang Guo, Yongcun Wang, Wenshan Yang, Tun Zong, Shuqi Anhui Univ Sci & Technol Sch Mech Engn Huainan Peoples R China State Key Lab Min Response & Disaster Prevent & Co Huainan Peoples R China Collaborat Innovat Ctr Min Intelligent Technol & E Huainan Peoples R China

Real-time and accurate obstacle detection is a vital technology for electric locomotives, especially as driverless vehicles are introduced. A method of obstacle detection for underground electric locomotive rail based on instance segmentation is developed to solve the problems of misdetection and missing detection, low detection accuracy, and slow detection speed of rail obstacles. The method of locating the track mask, demarcating the effective driving boundary, expanding the track mask, and forming the effective driving area is adopted to verify whether the target is an obstacle based on whether the target is located in the effective driving area, to avoid the problem of misdetection and missing detection of the target obstacle. The YOLACT++ (You Only Look At CoefficienTs) model is improved, and path augmentation and target classification loss function replacement strategies are adopted to enhance the model's ability to detect target details and increase the accuracy of target segmentation. Compared with traditional image processing, this method can detect both straight rail and turnout. The mean average precision of boundary box mAP0.5(box) and mask mAP0.5(mask) of the improved YOLACT++ model reaches 98.52% and 98.55%, which is higher than that of the YOLACT++ model, and the detection frame rate reaches 21.9 frames per second.

关键词： rail rail safety computer models system safety train

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