Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L<sup>2</sup>-Regularization

Zhai, Yikui; Deng, Wenbo; Xu, Ying; Ke, Qirui; Gan, Junying; Sun, Bing; Zeng, Junying; Piuri, Vincenzo

doi:10.1155/2019/9140167

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…Dai et al (2017) proposed a method for fundus image lesion recognition based on MS-CNN model, and found that the recognition accuracy of this method was 99.7% and the recall rate was 87.8% [6]. Zhai et al (2019) showed that data training in MS-CNN model could improve the extraction effect of robust features and avoid the occurrence of overfitting [7]. Therefore, in order to meet the demand of automatic recognition of commodities, and to improve the recognition efficiency and save costs, I conducted the research on intelligent CIR technology based on deep learning algorithm to meet the application demand of automatic recognition of individual merchants.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.

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Section: Introductionmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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“…where L(w) is the original Loss_Func, w i is the ith weight in the network, n is the total number of weights, and λ is the regularization strength. • L2 regularization: This technique adds a penalty to the Loss_Func proportional to the square of the weights, which encourages the model to have small weights and can help to prevent overfitting [115][116][117][118]. The regularized Loss_Func for L2 regularization can be written as:…”

Section: Regularization Techniquesmentioning

confidence: 99%

Convolutional Neural Networks: A Survey

Krichen

2023

Computers

145

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Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

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“…Compared with the fewest parameters-based networks for SAR image classification, this architecture was deeper with only about 10% of parameters. An efficient transferred max-slice CNN with L2-regularization term was proposed in [409] for SAR-ATR, which could enrich the features and recognize the targets with superior performance with small samples. An asymmetric parallel convolution module was constructed in [410] to avoid severe overfitting.…”

Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization

Cited by 16 publications

References 46 publications

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Convolutional Neural Networks: A Survey

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization

Cited by 16 publications

References 46 publications

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Convolutional Neural Networks: A Survey

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L²-Regularization