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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With the emergence of onboard vision processing for areas such as the internet of things (IoT), edge computing and autonomous robots, there is increasing demand for computationally efficient convolutional neural network (CNN) models to perform real-time object detection on resource constraints hardware devices.
In the midst of such critical circumstances created by a fire, this system is able to accurately inform the decision making process of firefighters in real time by extracting crucial information for processing.
We also show that limiting the use of contextual reasoning in learning the object detector acts as a form of defense that improves the accuracy of the detector after an attack.
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.