Infrared Detection of Substation Objects Using the Visible Light Spectrum

Energy losses in transmission lines can be aggravated by contact deterioration and corrosion, posing significant challenges to the reliability of energy distribution. Those conditions can be quickly identified using thermography, a popular non-invasive inspection method mandatory in many countries, with a minimum frequency determined by legislation. While recent advancements have focused on automating this process using artificial intelligence, working directly on the infrared domain remains a challenging task; not only assembling an infrared dataset requires specialists to properly annotate the images, making the task harder and more expensive, mainstream object detectors, made for the visible light domain, require modifications to work correctly on the infrared domain. Our proposed method allows using an object detector with results comparable to other works that performed detection directly on the infrared domain while requiring no significant changes to the model architecture. By employing stereo cameras, capable of collecting both the visible light spectrum and infrared, we can perform the detection on the visible light images and then transfer the box coordinates to the paired infrared image after rectifying the image pair to match.

Keywords:
substation automation; computer vision; object detection; infrared imaging; image rectification.

Novel method for object detection in infrared images using stereo cameras.

Rectification transfers detected boxes from the visible spectrum to infrared.

Easier and cheaper to build visible light spectrum datasets to train models on.

The annotated electrical substation dataset we made is publicly available.

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