Towards Real Time Object Detection
Found 9 free book(s)
Faster R-CNN: Towards Real-Time Object Detection with ...
papers.nips.ccFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren Kaiming He Ross Girshick Jian Sun Microsoft Research fv-shren, kahe, rbg, jiansung@microsoft.com Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R ...
You Only Look Once: Unified, Real-Time Object Detection
pjreddie.comized sensors, enable assistive devices to convey real-time scene information to human users, and unlock the potential for general purpose, responsive robotic systems. Current detection systems repurpose classifiers to per-form detection. To detect an object, these systems take a classifier for that object and evaluate it at various locations
You Only Look Once: Unified, Real-Time Object Detection
www.cv-foundation.orgized sensors, enable assistive devices to convey real-time scene information to human users, and unlock the potential for general purpose, responsive robotic systems. Current detection systems repurpose classifiers to per-form detection. To detect an object, these systems take a classifier for that object and evaluate it at various locations
Abstract
arxiv.orgonly having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes; it predicts de-tections for more than 9000 different object categories. And it still runs in real-time. 1. Introduction General purpose object detection should be fast, accu-
![Abstract arXiv:2103.02603v2 [cs.CV] 9 May 2021](/cache/preview/d/8/6/f/9/d/e/2/thumb-d86f9de271f3e441368a928ac5b32a7a.jpg)
Abstract arXiv:2103.02603v2 [cs.CV] 9 May 2021
arxiv.orgknowledge base. This would define a smart object detection system, and ours is an effort towards achieving this goal. The key contributions of our work are: •We introduce a novel problem setting, Open World Object Detection, which models the real-world more closely. •We develop a novel methodology, called ORE, based on
EfficientDet: Scalable and Efficient Object Detection
openaccess.thecvf.comtowards more accurate object detection; meanwhile, state-of-the-art object detectors also become increasingly more expensive. For example, the latest AmoebaNet-based NAS-FPN detector [42] requires 167M parameters and 3045B FLOPs (30x more than RetinaNet [21]) to achieve state-of-the-art accuracy. The large model sizes and expensive com-
Florence: A New Foundation Model for Computer Vision
arxiv.orgcation) to fine-grained (e.g. object detection), 2) Time: from static (e.g. images) to dynamic (e.g. videos), and 3) Modal-ity: from RGB only to multiple senses (e.g. captioning and depth). Due to the diversity nature of visual understanding, we …
Towards End-to-End License Plate Detection and …
openaccess.thecvf.comTowards End-to-EndLicense Plate Detection and Recognition: A Large Dataset and Baseline Zhenbo Xu1,2[0000−0002−8948−1589], Wei Yang 1( )[0000−0003−0332−2649], Ajin Meng1,2, Nanxue Lu1,2, Huan Huang2, Changchun Ying2, and Liusheng Huang1 1 School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
Time of Flight Principles, Challenges, and Performance …
www.st.comTime of Flight Basics Active illumination system: • Laser emits light (photons) towards a target • Light (partially) reflected from the target • Sensor area determines when light (photons) arrives • ToF is translated into distance Distance Value = Photon travel time/2 x by speed of light