Abstract
Construction waste is a serious problem that should be addressed to protect environment and save resources, some of which have a high recovery value. To efficiently recover construction waste, an online classification system is developed using an industrial near-infrared hyperspectral camera. This system uses the industrial camera to capture a region of interest and a hyperspectral camera to obtain the spectral information about objects corresponding to the region of interest. The spectral information is then used to build classification models based on extreme learning machine and resemblance discriminant analysis. To further improve this system, an online particle swarm optimization extreme learning machine is developed. The results indicate that if a near-infrared hyperspectral camera is used in conjunction with an industrial camera, construction waste can be efficiently classified. Therefore, extreme learning machine and resemblance discriminant analysis can be used to classify construction waste. Particle swarm optimization can be used to further enhance the proposed system.
Highlights
Recent technical developments have been focusing on protecting the environment and saving resources
Several studies have discussed the hazardous effects of accumulating construction waste [1]
This study mainly aimed to study the classification of construction waste
Summary
Recent technical developments have been focusing on protecting the environment and saving resources. Construction waste includes materials such as plastics, concrete, wood, and bricks that can degrade the environment; these materials can be extremely valuable when recycled or repurposed because they are manufactured via complicated procedures. They are composed of limited nonrenewable resources. Some studies have used near-infrared hyperspectral camera to classify different objects [2,3] This camera can capture the internal composition of objects by exploiting the DN values in different wavebands. The name hyper emanates from the camera’s ability to capture hundreds of wavebands with high resolution By analyzing these DN values, objects can be classified based on their composition.
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