Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Li, Xiufeng; Wen, Zheng; Hua, Qiaozhi

doi:10.1155/2022/3627246

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Aiming at the problems mentioned above, this study proposes a lightweight neuralnetwork-based method for recognizing edible fungi diseases in the fruit body period. This paper improves the ShuffleNetV2 network based on the fusion of an attention mechanism and obtains the ShufflenetV2-Lite+SE model [42][43][44][45][46][47], which is used to recognize edible fungi diseases in the fruit body period. This method provides certain technical guidance for the recognition of edible fungi diseases.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

et al. 2023

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Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi.

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Section: Introductionmentioning

confidence: 99%

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

et al. 2023

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Internet of Things Technology Application in Intelligent Transportation

Ushakov,

Nurzhaubayeva,

Okhotnikov

et al. 2024

Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East (AFE-2022)

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Optimizing urban eco-efficiency: Exploring the interplay of data analytics, iot integration, and cybersecurity in smart cities of the UAE

Onyusheva,

Dudukalov,

Shatila

et al. 2024

E3S Web of Conf.

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This study investigates the critical interrelationships between data analytics, Internet of Things (IoT) integration, and cybersecurity, and their collective impact on the efficiency of smart cities in the United Arab Emirates (UAE). Despite significant investments in smart city technologies, the complex dynamics among these factors remain underexplored, particularly in the context of the UAE’s unique urban settings. This research aims to fill this gap by conducting a comprehensive analysis to understand how these technologies interact and contribute to urban management and service delivery enhancements. Through quantitative methodologies involving a cross-sectional survey of 350 respondents from various sectors, this study utilizes inferential statistics to analyze the correlation and regression relationships among the key variables. The findings suggest a strong positive association between the integration of IoT devices, robust data analytics, and stringent cybersecurity measures, and the overall operational efficiency of smart cities. These insights provide valuable implications for policymakers, urban planners, and IT practitioners aiming to leverage smart technologies to foster more livable, efficient, and resilient urban environments.

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Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Cited by 3 publications

References 29 publications

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

Internet of Things Technology Application in Intelligent Transportation

Optimizing urban eco-efficiency: Exploring the interplay of data analytics, iot integration, and cybersecurity in smart cities of the UAE

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