Single Line Electrical Drawings (SLED): A Multiclass Dataset Benchmarked by Deep Neural Networks

Bhanbhro, H. and Hooi, Y.K. and Zakaria, M.N.B. and Hassan, Z. and Pitafi, S. (2023) Single Line Electrical Drawings (SLED): A Multiclass Dataset Benchmarked by Deep Neural Networks. In: UNSPECIFIED.

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Single-line drawings have diverse applications across various industries, including electrical substations, buildings, power distribution, maintenance, and more. Analyzing and interpreting these diagrams using deep neural networks requires the creation of large datasets, which poses a challenging task. The complexity of these diagrams, combined with the limited availability of publicly accessible datasets, makes dataset creation even more difficult. The quality of the dataset significantly impacts the classification accuracy, as weaker datasets can negatively affect the overall performance of the model. To address these challenges, we introduce Single Line Electrical Diagrams (SLED), a multiclass dataset consisting of 3,078 instances of engineering symbols. These symbols are extracted from intricate technical drawings known as Single Line Diagrams (SLDs). The SLED dataset is meticulously curated by annotating and pre-processing appropriate images to represent various symbol classes present in the diagrams. In this article, we benchmark the SLED dataset using a variant of the You Only Look Once (YOLO) algorithm, specifically YOLO v5, for symbol classification. The results of image classification on this newly generated dataset are promising. However, further improvement can be achieved by addressing class distribution imbalances within the dataset. By making this dataset available to the academic community, we aim to enhance the understanding of the field and shed light on an important yet neglected problem within the industry. We provide a comprehensive analysis of the dataset's characteristics and demonstrate the performance of deep learning models on recently created datasets. Our conclusions offer insights into the potential directions for future research in this domain. © 2023 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0; Conference of 13th IEEE International Conference on System Engineering and Technology, ICSET 2023 ; Conference Date: 2 October 2023; Conference Code:194131
Uncontrolled Keywords: Classification (of information); Electric power distribution; Electric substations; Graphic methods; Large dataset, Diverse applications; Electrical substations; Engineering drawing; Large datasets; Performance; Power distribution maintenance; Publicly accessible; Single line drawings; Single-line diagram; YOLOv5, Deep neural networks
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 11 Dec 2023 03:08
Last Modified: 11 Dec 2023 03:08

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