Study on Text Detection and Positioning Method of Utility Pole Identification Plate Based on Improved EAST

Lin, Shaofu; Wei, Qianwen

doi:10.1109/itnec48623.2020.9084779

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 2020

DOI: 10.1109/itnec48623.2020.9084779

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Study on Text Detection and Positioning Method of Utility Pole Identification Plate Based on Improved EAST

Shaofu Lin

Qianwen Wei

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2024

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Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Ukiwe,

Adeshina,

Jacob

et al. 2024

Journal of Electrical Systems and Inf Technol

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Hotspots in electrical power equipment or installations are a major issue whenever it occurs within the power system. Factors responsible for this phenomenon are many, sometimes inter-related and other times they are isolated. Electrical hotspots caused by poor connections are common. Deep learning models have become popular for diagnosing anomalies in physical and biological systems, by the instrumentality of feature extraction of images in convolutional neural networks. In this work, a VGG-16 deep neural network model is applied for identifying electrical hotspots by means of transfer learning. This model was achieved by first augmenting the acquired infrared thermographic images, using the pre-trained ImageNet weights of the VGG-16 algorithm with additional global average pooling in place of conventional fully connected layers and a softmax layer at the output. With the categorical cross-entropy loss function, the model was implemented using the Adam optimizer at learning rate of 0.0001 as well as some variants of the Adam optimization algorithm. On evaluation, with a test IRT image dataset, and a comparison with similar works, the research showed that a better accuracy of 99.98% in identification of electrical hotspots was achieved. The model shows good score in performance metrics like accuracy, precision, recall, and F1-score. The obtained results proved the potential of deep learning using computer vision parameters for infrared thermographic identification of electrical hotspots in power system installations. Also, there is need for careful selection of the IR sensor’s thermal range during image acquisition, and suitable choice of color palette would make for easy hotspot isolation, reduce the pixel to pixel temperature differential across any of the images, and easily highlight the critical region of interest with high pixel values. However, it makes edge detection difficult for human visual perception which computer vision-based deep learning model could overcome.

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Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Ukiwe,

Adeshina,

Jacob

et al. 2024

Journal of Electrical Systems and Inf Technol

View full text Add to dashboard Cite

show abstract

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Study on Text Detection and Positioning Method of Utility Pole Identification Plate Based on Improved EAST

Cited by 1 publication

References 13 publications

Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

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