OpenCV – ISS Art Blog | AI | Machine Learning

Introduction

Cracks on the surface are a major defect in concrete structures. Early crack detection allows preventing possible damage. There are various approaches to solving this problem. It can be manual inspection or automatic detection methods. But nowadays automatic detection methods include not only laser testing and radiographic testing. Progress in neural networks and computer vision allows us to use image processing for concrete surface crack detection.

In this article, we will share our approach to solving the problem mentioned above.

ChartRecorder: Computer Vision for Gas Flow Charts

Oct 29 2018

Flow charts are widely used in gas industry. They record parameters of a gas flow (like pressure) on a paper disc. This data is then used to calculate gas volume passed through a pipe.

The chart recorder contains two or three pens driven by sensors. These pens plot traces on a rotating paper disk. The disk performs one revolution per day or week, rarely per month. The disk is replaced after one revolution.

Currently disks are processed manually. The operator uses the vector graphics editor to convert the scanned bitmap to contours and then to tabular data. This approach relies heavily on the operator’s attention and experience. There is a high probability of mistakes. After all, such job is really boring!

We have developed an algorithm that performs this job automatically. Given a bitmap with a scanned chart, it produces tabular representation of chart data.
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Concrete crack detection using Deep Learning and Computer Vision

Introduction

ChartRecorder: Computer Vision for Gas Flow Charts

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