3D head reconstruction solution
Our client is a creative design agency that provides branding, advertising, graphic design, and virtual modeling services for individuals and corporate customers. The agency has its own in-house souvenir production.
Japan is a country with increased seismic activity. It is important to ensure the stability of structures during construction. The goal of this project was to automate the marking of reinforcing bars using an AI solution.
As we know, Japan is a country with increased seismic activity. It means it’s very important to ensure the stability of structures during construction. The main goal of our client’s project was to automate the marking of reinforcing bars that provide construction stability using an AI solution.
As a rule, in order to demonstrate the availability of the required number of reinforcing bars, builders have to attach colored markers to each section of the reinforcing mesh. Markers are attached horizontally and vertically.
Plastic markers often fall off. In addition, the marking process has to be repeated hundreds of times. Together with colored markers, builders attach a ruler for a photo to measure the distance between reinforcing bars.
It is physically difficult and time-consuming to carry out this work on marking reinforcing bars and calculating the distance.
As a rule, in order to demonstrate the availability of the required number of reinforcing bars, builders have to attach colored markers to each section of the reinforcing mesh. Markers are attached horizontally and vertically.
Plastic markers often fall off. In addition, the marking process has to be repeated hundreds of times. Together with colored markers, builders attach a ruler for a photo to measure the distance between reinforcing bars.
It is physically difficult and time-consuming to carry out this work on marking reinforcing bars and calculating the distance. Our application helps to automate the control over the location of reinforcing rods on construction sites.
The main challenges:
To create this automated system, the CHI Software development team took the following steps:
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As an ML/CV Engineer, I was responsible for tasks related to Computer Vision. The main goal of the project was to automate the process of detecting rebars in the image of the construction and to estimate the distance between rebars. To create such an automated system, the following steps were taken: - we used image pre-processing techniques based on a classical CV to reduce noise and remove unnecessary objects from the image, highlight main rebars, and separate them from inappropriate rebars; - we used lines Detection with Hough Transform to detect rebars on images with high quality and fast inference; - we used different color spaces and morphological image processing to detect tape without deep learning models; - we wrote a lot of logic for post-processing to filter and select only appropriate rebars; - we created automatic distance calculations between rebars. We delivered a working app within 3 months. Our team consisted of 5 experienced engineers and a PM. We successfully implemented an application that helps to reduce the time and effort that builders need to check the reliability of reinforcement structures.
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