Construction waste classification method based on CNN

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...

AI Technical Summary

Problems solved by technology

However, in practical applications, the recognition range and recognition rate of the above methods cannot meet the actual needs of the industry for classifying various construction wastes.

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