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infrared small target detection Algorithm Based on ISTD-CenterNet

Computers, Materials & Continua 2023年第12期77卷 3511-3531页

作者： Ning Li Shucai Huang Daozhi Wei Air and Missile Defense College Air Force Engineering UniversityXi’an710051China

This paper proposes a real-time detection method to improve the infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex *** method eliminates the need for an anchor frame,addressing the issues of low accuracy and slow *** is used as the framework for feature extraction,and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects.A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared ***,an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps,and a convolutional attention mechanism module is used to increase network stability and convergence *** experiments conducted on the infrared small target data set *** experiments show that compared to the benchmark network CenterNet-HRNet,the proposed ISTD-CenterNet improves the recall by 22.85%and the detection accuracy by 13.36%.Compared to the state-of-the-art YOLOv5small,the ISTD-CenterNet recall is improved by 5.88%,the detection precision is improved by 2.33%,and the detection frame rate is 48.94 frames/sec,which realizes the accurate real-time detection of small infrared targets.

关键词： infrared small target detection CenterNet data enhancement feature enhancement attention mechanism

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Using deep learning to detect small targets in infrared oversampling images

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Journal of Systems Engineering and Electronics 2018年第5期29卷 947-952页

作者： LIN Liangkui WANG Shaoyou TANG Zhongxing Shanghai Institute of Satellite Engineering Shanghai 201109China

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3–4 orders of magnitude, and has more powerful target detection performance.

关键词： infrared small target detection oversampling deep learning convolutional neural network(CNN)

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A Novel Filtering-Based detection Method for small targets in infrared Images

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Computers, Materials & Continua 2024年第11期81卷 2911-2934页

作者： Sanxia Shi Yinglei Song School of Automation Jiangsu University of Science and TechnologyZhenjiang212001China

infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense ***,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex *** paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering *** method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational *** the first step,the infrared image is filtered by a new filter kernel and the results of filtering are *** the second step,an adaptive thresholding method is utilized to determine the pixels in small *** the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection *** results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional *** with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution.

关键词： Gaussian filtering infrared small target detection fuzzy C-means clustering robustness

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Novel detection method for infrared small targets using weighted information entropy

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Journal of Systems Engineering and Electronics 2012年第6期23卷 838-842页

作者： Xiujie Qu He Chen Guihua Peng School of Information and Electronics Beijing Institute of Technology Beijing 100081 P. R. China

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

关键词： infrared small target detection local mutation weight-ed information entropy (LMWIE) grey value of target adaptivethreshold.

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