Crack Detection using YOLOv7 Segmentation Demo

In this video, we demonstrate Crack Detection, and no, I'm not referring to a specific type of addictive substance. This type of crack is the annoying one that you see almost all around your home and on your walls. If you have seen them, then you probably may have been tempted to peel off some of the paint, depending on how bad it is.

Regardless of your obsession; with the help of the YOLOv7 segmentation model, we are able to identify cracks in real-time. Credit to Joel Nadar for the implementation and demo.

=I want to learn this= 📑You can check out his Article here - https://medium.com/augmented-startups/yolov7-segmentation-on-crack-using-roboflow-dataset-f13ae81b9958 ⭐️If you want to learn how to train your own models from scratch and become a YOLOv7 Ninja🥷🏼, then you can enroll in the BEST (& and Most Comprehensive) YOLO Resouce on the Planet right over here - 📸 https://www.augmentedstartups.com/yolo-plus

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=Why though?== Well, detecting cracks autonomously is fancy but what problem does it solve hmmm? Great question! In mining, where a crack in a conveyor belt may propagate over time. If that same belt which may be carrying precious materials like gold & blood diamonds were to snap... then... Oh, Snap, there goes a million(maybe a billion) dollars! This is just one such application, but we can provide many more examples.

#yolov7 #computervision #deeplearning

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✅YOLOv7 surpasses all known object detectors (speed & accuracy) ✅56 FPS V100, 55.9% AP ✅120% faster than YOLOv5 ✅State-of-the-Art

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