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Chip and System Design of Real Time Object Detection Based on LS-R-YOLO Network

In this paper, point cloud and image feature distribution algorithms are adopted. First, we use the depth information of the LiDAR point cloud to perform object segmentation. Secondly, we use the result of object segmentation to find the ROI (region of interest) on the color image and set it as the input of the R-YOLO neural network for object detection. Finally, our algorithm will simultaneously obtain LiDAR point cloud and image recognition results in real time. In addition, we will use a cell-based design flow to implement high-speed convolution chips to improve system performance.