Context-Aware Data Augmentation for LIDAR 3D Object Detection

For 3D object detection, labeling lidar point cloud is difficult, so data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-sample effectively improves detection performance by inserting groundtruths into the lidar frame during training. However, these samples are often placed in unreasonable areas, which misleads model to learn the wrong context information between targets and backgrounds. To address this problem, in this paper, we propose a context-aware data augmentation method (CA-aug) , which ensures the reasonable placement of inserted objects by calculating the "Validspace" of the lidar point cloud. CA-aug is lightweight and compatible with other augmentation methods. Compared with the GT-sample and the similar method in Lidar-aug(SOTA), it brings higher accuracy to the existing detectors. We also present an in-depth study of augmentation methods for the range-view-based(RV-based) models and find that CA-aug can fully exploit the potential of RV-based networks. The experiment on KITTI val split shows that CA-aug can improve the mAP of the test model by 8%.Comment: 6 pages, 4 figure

Interface engineering of MXene towards super-tough and strong polymer nanocomposites with high ductility and excellent fire safety

The integration of high strength, high toughness, and excellent flame retardancy in polymer materials is highly desirable for their practical applications in the industry. However, existing material design strategies often fail to realize such a performance portfolio because of mutually exclusive mechanisms between strength and toughness, and low flame retardancy efficiency of nanofillers in polymers. Here, we reported the preparation of a multifunctional nanohybrid, Ti3C2Tx@MCA, by engineering the surface of titanium carbide nanosheets (Ti3C2Tx, MXene) with melamine cyanurate (MCA) via hydrogen bonding interactions, and subsequent thermoplastic polyurethane (TPU)/Ti3C2Tx@MCA nanocomposites. The resultant TPU nanocomposite containing 3.0 wt% of Ti3C2Tx@MCA shows a high tensile strength of 61.5 MPa, a toughness as high as 175.4 ± 7.9 MJ m−3 and a high strain at failure of 588%, and 40% reduction in the peak of heat release rate. Such extraordinary mechanical and fire retardant performances are superior to those of its previous counterparts. Interfacial hydrogen bonding in combination with the “labyrinth” effect and catalytic action of 2D Ti3C2Tx nanosheets are responsible for the outstanding mechanical and fire retardancy properties of TPU nanocomposites. This work provides a new paradigm for integral design of high-performance polymeric materials with excellent mechanical and fire-safe performances portfolio