Abstract
A large proportion of construction waste has a high recovery value, and some of the existing recycling-classification methods rely mainly on physical properties for vibration screening. In order to effectively recover construction waste, an industrial near-infrared hyperspectral camera is proposed in this paper to distinguish the spectral characteristics of the objects. The testing results were verified using an extreme learning machine, an adaptive-learning multilayer perceptron, and a one-dimensional convolutional neural network classification model. By establishing several different models to classify and identify the same kinds of experimental materials, the experimental results not only output the correct recognition rate, but also use the recognition efficiency and stability as indices for comprehensive evaluation. The results show that different classification models have different efficiencies and levels of correctness. Under different analytical conditions, such as when using data from different bands, it is very important to select the appropriate classification model to classify construction waste.
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