High-accuracy infrared simulation model based on establishing the linear relationship between the outputs of different infrared imaging systems

Bingtao, Guo; Wang, Xiaorui; Chen, Yujiao; Li, Zhaohui; Zhang, Jianlei

doi:10.1016/j.infrared.2015.01.010

Cited by 5 publications

(3 citation statements)

References 20 publications

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“… Feature extraction. Extract visual features of candidate regions, such as features such as SIFT (Scale-Invariant Feature Transform) [13] commonly used in common object detection, and extract features for each region.…”

Section: Traditional Object Detection Algorithmmentioning

confidence: 99%

Maritime ship detection algorithm based on improved YOLOv4

Chen¹,

Wang³

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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Maritime ship detection technology has important value in both the military field and maritime supervision. In terms of traditional detection method of maritime ship with low accuracy under complicated situations, in this paper, we adopt a new detection approach based on the improvement of YOLOv4 in order to realize automatic testing of maritime ship under complex circumstances by deep learning. It aims to adopt lightweight network GhostNet as features to extract the network. Depth-separable convolution will be converted to pointwise convolution first and then transformed into depthwise convolution. The network parameter will be reduced while ensuring the accuracy of testing. The accuracy of testing of maritime ship will be further improved by revising activation function as SMU, combining lose function Alpha-IoU and redesigning lose function CIOU. In order to verify the performance of the algorithm in foggy environment, the interference of foggy weather environment is fully considered when generating the training dataset of maritime ships. During training, Mosaic data enhancements were added to the samples to enhance experimental robustness. The loss function was improved using label smoothing techniques to prevent overfitting. Experimental results showed that when the confidence level is 0.5, compared with the original YOLOv4, the average accuracy of the proposed algorithm reaches 99.97% when the number of parameters is reduced by nearly 84.92%. When the ship target is tiny, the testing result is also highly accurate. Therefore, the method can meet the accuracy requirements of real-time processing of maritime vessel detection.

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Section: Traditional Object Detection Algorithmmentioning

confidence: 99%

Maritime ship detection algorithm based on improved YOLOv4

Chen¹,

Wang³

et al. 2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…Traditional object detection algorithms mainly include the deformable parts model (DPM) ( Dollár et al., 2009 ), selective search (SS) ( Uijlings et al., 2013 ), Oxford-MKL ( Vedaldi et al., 2009 ), and NLPR-HOGLBP ( Yu et al., 2010 ), etc. Traditional object detection algorithm basic structure mainly includes the following three-part: 1) region selector, first, a sliding window of different sizes and proportions is set for a given image, and the entire image is traversed from left to right and top to bottom to frame a specific part of the image to be detected as a candidate region; 2) feature extraction, extract visual features of candidate regions, such as scale-invariant feature transform (SIFT) ( Bingtao et al., 2015 ), Haar ( Lienhart and Maydt, 2002 ), histogram of oriented gradient (HOG) ( Shu et al., 2021 ) commonly used in face and standard object detection, and other features to extract features for each region; 3) classifier classification, use the trained classifier to identify the target category of the feature, such as the commonly used deformable part model (DPM), adaboot ( Viola and Jones, 2001 ), support vector machines (SVM) ( Ashritha et al., 2021 ) and other classifiers. However, these three parts achieved certain results while exposing their inherent flaws, such as using a sliding window for region selection will result in high time complexity and window redundancy, the uncertainty of illumination change and the diversity of background will result in poor robustness of the guide design feature technique ( Cao et al., 2020a ), poor generalization, and complex algorithm stages will result in slow detection efficiency and low accuracy ( Wu et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Nawaz

Bhatti

et al. 2022

Front. Plant Sci.

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Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

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“…For flight vehicles, aerothermal responses are crucial in the testing of flight parameters for an infrared radiation tracking measurement system, such as coordinate, attitude, and yaw [1][2][3][4]. Furthermore, aerothermal analysis methods are also essential to design the thermal protection structure and infrared stealth technology [5][6][7][8][9]. However, it is hard to accurately assess the aeroheating characteristics due to physical uncertainties and time-varying properties.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Aerothermal Analysis of a Cone-Cylinder Flight Body

Zhang

Jia

Mkumbuzi

et al. 2020

International Journal of Aerospace Engineering

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Exploring the aerothermal characteristic of a flight body has great military applications in tracking, locating, thermal protection, and infrared stealth technologies. Available studies are mostly focused on the transient aerothermal characteristics of vehicles in some specific flight datum, which are not able to satisfy the requirements in real-time tracking for an infrared system. This paper probes into a method of dynamic thermal analysis of a cone-cylinder flight body with a high spinning speed. Firstly, a theoretical model for analyzing the dynamic aerothermal characteristics is established using the thermal node-network method. Then, trajectory datum and the convective heat-transfer coefficients are solved simultaneously. Besides, the trajectory datum in supersonic, transonic, and subsonic regimes is separately defined as the boundary conditions, and fluid-thermal analysis methods are implemented by a combination of sliding mesh and multicoordinate approaches. Finally, the flow characteristics are analyzed and compared with disregarding the rotational speed. The results demonstrate that there are significant differences between the two cases, especially at the high-speed regimes. This study further confirms that it is essential to conduct the aerothermal analysis from a dynamic point of view, and taking the impacts of coupling motion into account is also of vital importance.

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High-accuracy infrared simulation model based on establishing the linear relationship between the outputs of different infrared imaging systems

Cited by 5 publications

References 20 publications

Maritime ship detection algorithm based on improved YOLOv4

Maritime ship detection algorithm based on improved YOLOv4

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

Dynamic Aerothermal Analysis of a Cone-Cylinder Flight Body

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