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pro vyhledávání: '"object detection systems"'
Autor:
Wang, Chien-Yao, Liao, Hong-Yuan Mark
This is a comprehensive review of the YOLO series of systems. Different from previous literature surveys, this review article re-examines the characteristics of the YOLO series from the latest technical point of view. At the same time, we also analyz
Externí odkaz:
http://arxiv.org/abs/2408.09332
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against these attacks is of the utmost importance when designing computer vision a
Externí odkaz:
http://arxiv.org/abs/2312.00173
In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR offers precise 3D spatial sensing information but struggles in adverse weather like fog. Conversely, radar signals can penetrate rain or mist due to their specific
Externí odkaz:
http://arxiv.org/abs/2211.06108
This study examines the relationship between H.264 video compression and the performance of an object detection network (YOLOv5). We curated a set of 50 surveillance videos and annotated targets of interest (people, bikes, and vehicles). Videos were
Externí odkaz:
http://arxiv.org/abs/2211.05805
Object detection systems using deep learning models have become increasingly popular in robotics thanks to the rising power of CPUs and GPUs in embedded systems. However, these models are susceptible to adversarial attacks. While some attacks are lim
Externí odkaz:
http://arxiv.org/abs/2208.07174
Akademický článek
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Autor:
Yu, Yinan, Scheidegger, Samuel, Grönvall, John-Fredrik, Palm, Magnus, Svanberg, Erik, Wennerby, Johan Amoruso, Bakker, Jörg
Single-vehicle accidents are the most common type of fatal accidents in Sweden, where a car drives off the road and runs into hazardous roadside objects. Proper installation and maintenance of protective objects, such as crash cushions and guard rail
Externí odkaz:
http://arxiv.org/abs/2205.01783
Publikováno v:
2021 10th Mediterranean Conference on Embedded Computing (MECO)
Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles
Externí odkaz:
http://arxiv.org/abs/2106.04146
Akademický článek
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The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems. Real-time object recognition on the edge is one of the representative deep neural network (DNN) powered edge systems for r
Externí odkaz:
http://arxiv.org/abs/2004.04320