Infrared Target Detection and Recognition Method in Airborne Photoelectric System

Ding, Meng; Sun, Zhejun; Wei, Li; Cao, Yunfeng; Yao, Yuheng

doi:10.2514/1.i010655

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…At the same time, the traditional machine learning cannot be applied to the processing of large datasets, and it is difficult to realize the optimization of feature design, feature selection, and model training, which makes the classification effect of the model poor. erefore, image classification methods using traditional machine learning are affected in many application fields [7]. Research shows that because texture, shape, and color features can be used for image classification and recognition, low-level basic features can be used as the basis of image classification.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model of Image Classification Using Machine Learning

Lv¹,

Zhang²,

Wang³

2022

Advances in Multimedia

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Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Model of Image Classification Using Machine Learning

Lv¹,

Zhang²,

Wang³

2022

Advances in Multimedia

View full text Add to dashboard Cite

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“…The literature introduces the comparison of infrared target imaging and recognition technology: if the target itself is small, or the distance between the target and the non-imaging system is too far, the imaging area of the target is small, and only visible points or point vision, infrared target detection is more complicated [ 11 ]. The literature introduces the classification and recognition of surface infrared technology based on vector machines: the image area of surface targets is much larger than the target infrared imaging system, and the same person wearing different clothes has different characteristics to be observed [ 12 ]. The literature introduces that SVM + HOG supports a classification [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine

Zhang

2022

Computational Intelligence and Neuroscience

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In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. Therefore, using machine learning technology for fuzzy research has a good research prospect. In the development of related research, the author of this study noticed that some researchers began to use tennis machine learning technology and achieved good results. However, most of the research is only for simple analysis and is related to the current work. It cannot be used to move a solid tennis ball, nor it can make small changes to the original tennis movement; thus, it cannot carry out a complete and brand-new movement. The defense of tennis first establishes visual teaching tools with the help of various courses and visual teaching techniques to improve the teaching effect. By optimizing the network data, this study constructs the corresponding data search model, which downloads a large amount of data from the network ram, so as to separate the impact of the network environment on the load. The simulation results show that the model is optimized for the high-quality 3G network environment, and the load time and energy consumption are greatly reduced. It is more efficient in WiFi and a a high-quality 4G network environment.

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“…Second, in order to extract the features of each candidate region, he proposed a local descriptor and then used a simple linear classifier to further complete the target recognition. Finally, he transplanted the detection and recognition algorithm to the embedded platform [7]. Hatem HR uses protues software to simulate and implement the voice communication system.…”

Section: Related Workmentioning

confidence: 99%

Design of a Communication Device of Infrared Thermal Imaging Target Recognition and Tracking System

Zhang

Wang

2022

Advances in Multimedia

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Due to the presence of solar radiation, each object emits electromagnetic waves at different temperatures. Thermal infrared imaging is to image an object through a thermal infrared CCD, which can reflect the temperature field of the object. The image information is obtained by infrared radiation intensity distribution, and the infrared light invisible to the human eye is converted into a visible image. Thermal infrared has a wide range of applications in military, industrial, automotive, medical, and other fields. This technology is still a research hotspot at present, and it has high research value for any region. This paper designs a communication device architecture for an infrared thermal imaging target recognition and tracking system and optimizes and compares different target recognition and tracking algorithms. The test results show that the device system can reduce the system software frame processing time from 120 ms to an average of 28 ms, and the processing speed is increased by about 4.3 times.

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Infrared Target Detection and Recognition Method in Airborne Photoelectric System

Cited by 12 publications

References 17 publications

Deep Learning Model of Image Classification Using Machine Learning

Deep Learning Model of Image Classification Using Machine Learning

Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine

Design of a Communication Device of Infrared Thermal Imaging Target Recognition and Tracking System

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