Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

The accumulation of construction solid waste (CSW) leads to the waste of land resources and environmental pollution, becoming a significant social problem. Identifying the amount of high-value CSW is essential for assessing the value of accumulated CSW and formulating appropriate recycling strategies. With the development of machine learning technology, CSW recognition techniques combining image acquisition devices and convolutional neural networks have been widely applied. However, most technologies are based on 2D images, making it difficult to recognize high-value CSW in accumulated CSW. This study proposes a new method to identify high-value CSW using millimeter wave radar based on penetration properties of electromagnetic waves. First, efficient imaging of CSW was achieved by optimizing the imaging algorithm. Then, CSW were classified in combination with the selected convolutional neural network (CNN) method based on the dataset constructed in the lab. At last, high-value CSW was screened by normalizing the imaging algorithm. The findings indicate that the balance between imaging effect and efficiency is achieved when the step speed and height are 200 mm/s and 4 mm. The optimized imaging approach effectively captures images of CSW. Compared with SegNet and PSPNet, DeepLabv3+ can identify complete bricks and reinforcing bars precisely. The accuracy can reach 85.18 %. Moreover, the millimeter-wave radar can determine the location and size of waste and can potentially acquire three-dimensional and buried information about waste.