Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images

Polonskaia, Iana S.; Aliev, Ilya R.; Nikitin, Nikolay O.

doi:10.1016/j.procs.2021.10.021

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…We employed a CNN model that had been pre-trained, ResNet50, as the base model for our finetuning process. ResNet50 is a popular CNN architecture that has shown excellent performance in image recognition tasks [20]. The pre-trained ResNet50 model, which was trained on the ImageNet dataset, is the starting point for our DDoS attack detection model.…”

Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.

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Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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“…In image classification, Palacios Salinas et al, proposed a network architecture search (NAS) system optimised for classifying EO images with blocks that were pre-trained on four EO datasets (e.g., [16,[61][62][63]) by customising the search space of AutoKeras [64]. Another approach for object recognition in EO images was presented by Polonskaia et al, who proposed an automated evolutionary NAS approach for designing CNNs implemented in Auto-Pytorch [65].…”

Section: Automl For Eo Tasksmentioning

confidence: 99%

AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images

Wąsala,

Marselis,

Arp

et al. 2024

Remote Sensing

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Super-resolution (SR), a technique to increase the resolution of images, is a pre-processing step in the pipelines of applications of Earth observation (EO) data. The manual design and optimisation of SR models that are specific to every possible EO use case is a laborious process that creates a bottleneck for EO analysis. In this work, we develop an automated machine learning (AutoML) method to automate the creation of dataset-specific SR models. AutoML is the study of the automatic design of high-performance machine learning models. We present the following contributions. (i) We propose AutoSR4EO, an AutoML method for automatically constructing neural networks for SR. We design a search space based on state-of-the-art residual neural networks for SR and incorporate transfer learning. Our search space is extendable, making it possible to adapt AutoSR4EO to future developments in the field. (ii) We introduce a new real-world single-image SR (SISR) dataset, called SENT-NICFI. (iii) We evaluate the performance of AutoSR4EO on four different datasets against the performance of four state-of-the-art baselines and a vanilla AutoML SR method, with AutoSR4EO achieving the highest average ranking. Our results show that AutoSR4EO performs consistently well over all datasets, demonstrating that AutoML is a promising method for improving SR techniques for EO images.

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“…The image target detection method based on deep learning technology mainly relies on Convolutional Neural Networks (CNN) [2] to automatically extract image features and perform target recognition.From 2013 to the present, with the increase in computing power and the availability of massive data, CNN has made significant progress in the field of vehicle object recognition. During this period, various network structures such as VGGNet [3] , GoogLeNet [4] , ResNet [5] , and detection algorithms like R-CNN, YOLO, SSD, etc. have emerged, achieving important breakthroughs in improving the accuracy and speed of vehicle object recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al [6] proposed a Faster R-CNN [7] based method to detect vehicle targets for highway congestion scenarios. The use of multi-variation processing module and the loss of repulsive force increases the model generalization capability while complementing the data diversity and improving the dense vehicle detection accuracy .Aiqing Huo et al [8] proposed an improved YOLOv3 algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hardware acceleration for real-time vehicle object detection based on MPSoC *

geng,

ding

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Facing the growing demand for autonomous driving, the accuracy of vehicle target detection has become a crucial issue. Given the challenges of mainstream target detection algorithms on image processors, such as large model parameters, poor real-time performance, high power consumption, and high cost, this paper proposes a real-time vehicle target detection scheme based on MPSoC. By replacing CSPdarknet53 in YOLOV4 with MobilenetV3, lighter feature extraction is achieved. PANET structure is used for feature fusion, and depthwise separable convolution is incorporated. Channel Attention (CA) module is adopted to enhance detection accuracy. Ultimately, the model's floating-point parameters are quantized to 16-bit fixed-point numbers and deployed on the MPSoC. Experimental results on the Kitti vehicle dataset demonstrate that the proposed scheme achieves an precision of 81.97, an FPS of 47.23, and power consumption of 10W, increasing the detection speed by 359.6% compared to CPU and reducing power by 91.4% compared to GPU. The proposed scheme can satisfy the requirements of real-time target detection and can be deployed on power-limited devices such as intelligent vehicles.

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Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images

Cited by 8 publications

References 8 publications

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images

Hardware acceleration for real-time vehicle object detection based on MPSoC *

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