ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique.

This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines.

Structural properties of Bi thin film grown on Si (111) by quasi-van der Waals epitaxy.

Crystallinity of an 80-nm-thick bismuth thin film grown on Si (111) substrate by MBE was investigated. The highly (0003) textured Bi film contains two twinning domains with different bilayer stacking sequences. The basic lattice parameters c and a as well as b, the bilayer thickness, of the two domains were determined from a series of X-ray diffraction (XRD) measurements, and found that the differences are within 0.1% as compared with those of bulk Bi reported in literature, suggesting that the Bi film has been nearly fully relaxed. From the XRD φ-scans of asymmetric Bi (01-14), (10-15), (11-26) planes and Si (220) plane as well as selected area electron diffraction patterns and electron back scatter diffraction pole figures, we confirmed the well registration between the lattices of Si and Bi lattice, i.e. the ω angle difference between Bi[0003] and Si[111] and the φ angle difference between Bi[01-14] and Si[220] are 0.056° and 0.25°, respectively, and thus concluded that the growth is a quasi-van der Waals epitaxy.