Electric Power Fuse Identification with Deep Learning

<strong>Paper Title</strong>: Electric Power Fuse Identification with Deep Learning <strong>Journal Title</strong>: IEEE Transactions on Industrial Informatics <strong>Accepted On</strong>: February 6, 2023 <strong>GitHub Link</strong>: https://github.com/MEDomics-UdeS/energAI-fuses <strong>Description</strong>: This project implements a supervised learning PyTorch-based end-to-end object detection pipeline for the purpose of detecting and classifying fuses in low-voltage electrical installations. <strong>Files</strong>: <strong>annotations.csv</strong>: Contains bounding box annotations for images. Save the file to 'data/annotations/' in the cloned GitHub repository. <strong>final_model.pkl</strong>: Final trained model. Save the file anywhere and select it using the GUI from the cloned GitHub repository to use it. <strong>images.zip</strong>: Images for learning and testing. Extract the .zip file to 'data/raw/' in the cloned GitHub repository. <strong>Abstract</strong>: As part of arc flash studies, survey pictures of electrical installations need to be manually analyzed. A challenging task is to identify fuse types, which can be determined from physical characteristics such as shape, color and size. To automate this process using deep learning techniques, a new dataset of fuse pictures from past arc flash projects and data from the web was created. Multiple experiments were performed to train a final model, reaching an average precision (AP50) of 91.06 % on the holdout set, which confirms its potential for identification of fuse types in new photos. By identifying fuse types using physical characteristics only, the need to take clear pictures of the label text is eliminated, allowing pictures to be taken away from danger, thereby improving the safety of workers. All the resources needed to repeat the experiments are openly accessible, including the code and datasets.