Construction waste classification method based on CNN

A construction waste and classification method technology, which is applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problem that the recognition rate cannot meet the actual needs of various construction waste classifications, and achieve sufficient feature extraction and classification accuracy High, the effect of improving the accuracy rate

Pending Publication Date: 2022-03-22
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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However, in practical applications, the recognition range and recognition rate of the above me

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  • Construction waste classification method based on CNN
  • Construction waste classification method based on CNN
  • Construction waste classification method based on CNN

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Embodiment Construction

[0029] The present invention is described in further detail below in conjunction with embodiment.

[0030] Such as figure 1 Shown: the present invention discloses the construction waste classification method based on CNN, combines figure 2 , including the following steps:

[0031] Step 1. Prepare data

[0032] Deep learning models often require a large number of data samples, and the neural network can only find stable and reliable parameters and models after training and continuous optimization of a large number of samples. Currently there is no open-source construction waste data set, so the data set used in the training and testing of the present invention is a self-made data set. Cameras are used to capture images of construction waste, and the collected image data is manually screened and marked. The proposed algorithm is mainly used on the conveyor belt of the automatic workshop of the garbage recycling factory. In order to facilitate the later data preprocessing an...

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Abstract

A construction waste classification method based on a CNN comprises the following steps that 1, construction waste pictures are collected, a construction waste material data set is self-made, and data set samples are enriched through data enhancement; step 2) using the lightweight self-made convolutional neural network model for construction waste classification: inputting the preprocessed pictures into the trained network model, and finally outputting a group of probability values, the category with the maximum probability value being a result category; 3) in the CNN, performing convolution operation on the image through a convolution kernel, extracting image feature information, in construction waste classification application, noise in data set image information is inevitable, and in order to reduce noise transmission, using a maximum pooling mode, and reducing feature image dimensions; and (4) an AdamOptimizer optimizer is used for optimizing model parameters, and therefore accurate classification of four types of construction waste materials including red bricks, foam, concrete and PVC is achieved. According to the invention, accurate classification of four types of construction waste materials is realized.

Description

technical field [0001] The invention relates to the technical field of construction waste classification, in particular to a CNN-based construction waste classification method. Background technique [0002] While the country is vigorously developing the construction industry, there are more and more construction wastes. The existing construction waste recycling devices have complex recycling procedures and low efficiency, resulting in recycling costs far higher than the value of the resources themselves. Therefore, we are exploring high-efficiency and low-cost Construction waste recycling equipment is particularly important. The invention uses a neural network algorithm to accurately classify the construction waste, thereby greatly improving the recovery efficiency of the construction waste recycling station and reducing economic costs. [0003] Aiming at the problem of construction waste recycling, some scholars propose to use features such as volume and weight to classify...

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Application Information

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IPC IPC(8): G06V20/00G06V10/20G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2431G06F18/214G06F18/2415
Inventor 宋琳袁山山马宗方赵慧轩宋琪
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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