Material identification of construction machinery based on multisource sensor information fusion

Abstract

In order to improve the recognition accuracy of construction machinery and equipment and materials in low contrast scenes, a construction machinery material recognition algorithm based on multisource sensor information fusion is proposed. In the paper, the millimeter wave radar is fused with the camera considering its strong penetration ability in rainy and foggy days and dim environments. Firstly, the spatial coordinates of radar and camera are unified by establishing a spatial fusion model of millimeter wave and camera; then the target acquired by millimeter wave is projected onto the image and the detection frame intersection and ratio model is used to generate the region of interest of the camera; finally, the improved YOLOv2 algorithm is used to identify the region of interest, and in the improved idea, the low‐level information is first connected with the high‐level information in multilayer depth. At the same time, a multiscale feature pyramid network structure is used to achieve recognition of objects of different scales. This model effectively reduces interference from other feature categories while improving the recognition efficiency of the system. The algorithm can effectively improve the recognition accuracy of mechanical materials in low‐contrast scenes, as demonstrated by the validation of different scenes.

Automatic segmentation of airport pavement damage by AM‐Mask R‐CNN algorithm

“…16 In recent years, with the development of artificial intelligence technology, deep learning has been widely used in aerospace, industrial production, engineering structure, and other fields. [17][18][19][20][21] Crucially, deep learning provides a fast and efficient method for pavement damage detection. [22][23][24][25] Cao et al proposed a seam crack detection method based on VGG16 deep convolutional neural network.…”