Intelligent identification system and method for building material categories
A technology for building materials and intelligent identification, applied in the field of intelligent identification, can solve problems such as difficult to accurately identify building materials, achieve the effects of reducing the impact of light, improving the recognition effect, and improving the accuracy rate
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[0018] Example 1
[0019] The embodiment of the intelligent recognition system for building material category of the present invention is basically as attached figure 1 As shown, it includes: an acquisition module, a preprocessing module, an extraction module, a clustering module, an identification module, a feedback module, a storage module, a learning module, and an output module.
[0020] The acquisition module is a CMOS image sensor, which acquires textures on building materials and sends the image to the preprocessing module. After the preprocessing module receives the image, it uses the Gaussian filtering algorithm to denoise the image, and sends the denoised image to the feature extraction module.
[0021] After the feature extraction module receives the image, it extracts the LBP texture feature of the image. Specific steps: Step one, find the basic local binary mode of each pixel of the window. Step two: cyclically shift the binary string to the right to obtain all possib...
Example Embodiment
[0024] Example 2
[0025] Compared with Embodiment 1, the only difference is that it also includes a storage module, a learning module, and a feedback module. The storage module is used to store the results of matching and recognition, and provide learning samples for the matching of texture features, so that the system can adaptively analyze new samples of building materials, thereby improving the accuracy of recognition. The learning module extracts the matching and recognition results in the storage module for learning, and optimizes the efficiency and accuracy of matching. Through learning, the matching error is corrected and the recognition accuracy is improved. The feedback module corrects the matching process through negative feedback. When the matching error is greater than the preset threshold, the matching is performed again until the error is less than the preset threshold. In this way, the matching result is fed back, so as to continuously modify the matching proce...
Example Embodiment
[0026] Example 3
[0027] Compared with Embodiment 1, the only difference is that it also includes a pressure sensor and a 3D camera. The pressure sensor is placed under the building materials and connected to the identification module via a wireless network. The pressure sensor is used to measure the weight of building materials, and send the measured weight data of the building materials to the identification module through a wireless network. The 3D camera is installed in the warehouse where the building materials are stored, and is connected to the identification module via a wireless network. The 3D camera is used to capture the length, width, and height dimensions of building materials, and send the captured length, width, and height dimensions of building materials to the recognition module (refer to the document "High-precision camera calibration algorithm in 3D measurement system") . In addition, the pressure sensor is also installed on the transportation tool, and th...
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