Abstract
Visual inspection of electrical utility assets is crucial in ensuring the continuous operation of a system or plant. With the advent of digital imagery using mobile devices, it has become easy to collect a vast amount of asset pictures from sites. To further enhance inspection efficiency, we propose RetinaNet, a deep learning-based object detection model that can be trained to automatically detect specific objects and features from images of outdoor industrial structures. The model is capable of detecting features such as intrusions, tree or bushes in the vicinity of the lattice towers. We also introduce a model training framework for use with very small datasets which consists of rigorous data augmentation, image pre-sizing, focal loss function, progressive resizing, learning rate finder, and the Ranger optimizer. Experiment results show that the proposed model used in conjunction with the aforementioned training framework results in the lowest validation loss and highest mean average precision of 31.36
Supported by Uniten R&D Seed Grant U-TS-RD-19-31.
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Baharuddin, M.Z., How, D.N.T., Sahari, K.S.M., Abas, A.Z., Ramlee, M.K. (2021). Object Detection Model Training Framework for Very Small Datasets Applied to Outdoor Industrial Structures. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2021. Lecture Notes in Computer Science(), vol 13051. Springer, Cham. https://doi.org/10.1007/978-3-030-90235-3_47
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