Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS.

The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images.

Unifying obstacle detection, recognition, and fusion based on millimeter wave radar and RGB-depth sensors for the visually impaired.

It is very difficult for visually impaired people to perceive and avoid obstacles at a distance. To address this problem, the unified framework of multiple target detection, recognition, and fusion is proposed based on the sensor fusion system comprising a low-power millimeter wave (MMW) radar and an RGB-Depth (RGB-D) sensor. In this paper, the Mask R-CNN and the single shot multibox detector network are utilized to detect and recognize the objects from color images.