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Brickspect

Automated defect detection
and height control
of paving blocks and hollow bricks
for construction material
manufacturers

  • About
  • Advantages
  • Defects detected
  • Technical Data
  • Gallery
Brickspect is a fully automated vision defect detection system for manufactures of the building materials.

In the construction industry due to the limited availability of human resources, more and more processes (previously unprofitable) are now being automated in the construction industry. Find our how Brickspect can support your production optimization.

Using vision cameras and machine learning algorithms that Brickspect is equipped with, the system effectively carries out the processes of detecting and removing defects in bricks or hollow blocks.

Brickspect is used to examine defects and the height of a product, especially when it is specific and hardly replicated. The system automatically detects damage on the upper surface of paving stones and blocks - cracks, discolorations, defects and others - and detects incorrect dimensions.

Brickspect algorithms for dimension analysis are based on laser triangulation.

Thanks to the machine learning algorithms, Brickspect allows collecting, reviewing and analyzing all data regarding the production process.
Advantages:
- testing the height of construction products with simultaneous identification of damage
- user-friendly touch panel interface with visual presentation of inspection results
- centralized qualitative and quantitative product control - collection, review and analysis of all data regarding the production process
- (optional) automatic generation of reports, trend analyzes and alarms

What do you gain?
- solving the problem of labor shortages by introducing automated machine vision based on artificial intelligence instead of manual inspection
- strengthening the reputation and trust in the brand - consistent delivery of high-quality products
- avoiding problems and costs in the further process (wholesale return, entire batch, container, etc.)
Brickspect automatically detects:
- cracks
- discoloration
- cavities
- incorrect dimensions
and other.
Construction of the Brickspect system:

- 3 laser triangulation sensors (for measuring the height of paving stones)
- 2 overhead cameras (for examining damage to the upper surface of paving stones)
- housing with a touch panel (control system enabling the machine to be operated via a touch panel)
- dedicated controller enabling integration with the production line control logic

Configurable interface and database
Based on the production specification system interface can be adjusted, including automatic system set-up with data upload by higher-order system (e.g. MES) and additional data upload by line operator. Data collected by the system can be automatically uploaded to higher-order system.

They trust our know-how:

  • In our opinion, the application of neural networks and the classification function for detected defects distinguishes KSM Vision's solution from competitive alternatives, and the system's achieved efficiency ensures quality control on the level of leading manufacturers' systems.
    MLEKOVITA, listed on the FORBES 100 ranking of the largest private Polish companies (2022).
    mgr. inż. Dariusz Sapiński,
    President of the Board
  • During the design process, KSM Vision demonstrated great flexibility in adapting the system to the expectations of BIOFARM's Production Department. (...) Since the implementation of the BLISPECT vision technology on BIOFARM's first production line nearly two years ago, KSM Vision's Service Department has been and remains available to BIOFARM employees with a good response time.
    BIOFARM Jarosław Pieczuro,
    President of the Board
  • KSM Vision Sp. z o.o. developed a system for quality control of glass vials for tablets. (...) A major advantage of the system is the use of machine learning algorithms, which allows for easy and quick system adjustment.
    ADAMED Group Dariusz Stępień,
    Director of Infrastructure and Media

Our R&D on the solutions with the use of neural network potential are backed by our renowned partners: