GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

Kukreja, Vinay; Kumar, Deepak; Kaur, Amandeep; Geetanjali, .; Sakshi, .

doi:10.1109/iceca49313.2020.9297625

Cited by 32 publications

(5 citation statements)

References 19 publications

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“…Future research should address the deficiencies mentioned above, dealing with the computational complexity of the solutions devised, and also addressing hardware-specific concerns that may affect their final performance, generalizing the study of energy consumption (approached punctually in [53,57,83]), as well as other interesting related matters, such as the proper use of parallelism strategies or how to exploit jointly modern multi-core architectures and AI acceleration hardware in a proper way. Likewise, in order to overcome data scarcity, it will be necessary to either explore techniques to alleviate or streamline the dataset creation process (e.g., synthetic data generation based on Generative Adversarial Networks [130,131], or image and video acquisition in simulated environments [132]), or devise DL alternatives that demand a smaller volume of data (e.g., the so-called few-shot learning techniques [133,134]). Finally, although the body of works considered in the study represents a broad spectrum of applications within ambient intelligence, it does not cover paradigmatic scenarios in the field, such as workplaces, educational centers, or smart homes.…”

Section: Discussionmentioning

confidence: 99%

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.

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Section: Discussionmentioning

confidence: 99%

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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“…Beside the meter recognition, data augmentation has been studied for other digit recognition scenarios. V. Kukreja et al [33] proposed the license plate augmentation method to solve the noise issue by applying Generative Adversarial network (GAN) to create high-resolution images from a lowresolution image. However, their method did not address the limited character and number variation problem.…”

Section: B Character Augmentationmentioning

confidence: 99%

Generative Data Augmentation for Automatic Meter Reading Using CNNs

2022

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“…Contrary to traditional biological assays, which are time-consuming and expensive, artificial intelligence (AI) models have appeared as a reliable option, being widely researched throughout these years. AI models can distinguish diseased plants from healthy ones based on classification algorithms, most common ones including logistic regression [2], CNN [3], Neural Networks [4], Support Vector Machines (SVM) [5], Decision Trees [6], k-Nearest Neighbors (k-NN) [7], and Naive Bayes [8].…”

Section: Introductionmentioning

confidence: 99%

Study on the automatic detection of pepper TSWV disease based on ResNet

Ming

2023

TNS

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This study focuses on the development of a binary classification model for Tomato Spotted Wilt Virus (TSWV) recognition in peppers. A diverse dataset is created for model training, which incorporates TSWV-infected pepper imagery with varying infection severity and regions of different sizes and resolution. The model is trained based on ResNet-18 architecture, resulting in excellent performance of metrics with scores reaching 0.990. Thus, the developed model may have applicational potential for pepper TSWV recognition in real-life conditions.

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GAN-based synthetic data augmentation for increased CNN performance in Vehicle Number Plate Recognition

Cited by 32 publications

References 19 publications

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

Generative Data Augmentation for Automatic Meter Reading Using CNNs

Study on the automatic detection of pepper TSWV disease based on ResNet

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