Real-time object detection is the task of doing object detection in real-time with fast inference while maintaining a base level of accuracy.
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The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers.
We propose a deep feature pyramid architecture which makes use of inherent properties of features extracted from Convolutional Networks by capturing more generic features in the images (such as edge, color etc.)
object detection framework plays crucial role in autonomous driving.
To address this problem, we propose a multiple receptive field and small-object-focusing weakly-supervised segmentation network (MRFSWSnet) to achieve fast object detection.
We then introduce a proposal generation network to predict 3D region proposals from the generated maps and further extrude objects of interest from the whole point cloud.
In this paper, we investigate the performance degradation of SNNs in the much more challenging task of object detection.
This paper introduces a live object recognition system that serves as a blind aid.
On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms.