Open AccessPosted Content
Light-Head R-CNN: In Defense of Two-Stage Object Detector.
TL;DR: The authors' ResNet-101 based light-head R-CNN outperforms state-of-art object detectors on COCO while keeping time efficiency and significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.
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Abstract: In this paper, we first investigate why typical two-stage methods are not as fast as single-stage, fast detectors like YOLO and SSD. We find that Faster R-CNN and R-FCN perform an intensive computation after or before RoI warping. Faster R-CNN involves two fully connected layers for RoI recognition, while R-FCN produces a large score maps. Thus, the speed of these networks is slow due to the heavy-head design in the architecture. Even if we significantly reduce the base model, the computation cost cannot be largely decreased accordingly.
We propose a new two-stage detector, Light-Head R-CNN, to address the shortcoming in current two-stage approaches. In our design, we make the head of network as light as possible, by using a thin feature map and a cheap R-CNN subnet (pooling and single fully-connected layer). Our ResNet-101 based light-head R-CNN outperforms state-of-art object detectors on COCO while keeping time efficiency. More importantly, simply replacing the backbone with a tiny network (e.g, Xception), our Light-Head R-CNN gets 30.7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy. Code will be made publicly available.
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Citations
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma,Xiangyu Zhang,Hai-Tao Zheng,Jian Sun +3 more
- 08 Sep 2018
TL;DR: ShuffleNet V2 as discussed by the authors proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs, based on a series of controlled experiments, and derives several practical guidelines for efficient network design.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Chien-Yao Wang,Hong-Yuan Mark Liao,Yueh-Hua Wu,Ping-Yang Chen,Jun-Wei Hsieh,I-Hau Yeh +5 more
- 14 Jun 2020
TL;DR: Cross Stage Partial Network (CSPNet) as discussed by the authors integrates feature maps from the beginning and the end of a network stage to mitigate the problem of duplicate gradient information within network optimization.
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
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Object Detection in 20 Years: A Survey
TL;DR: This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019), and makes an in-deep analysis of their challenges as well as technical improvements in recent years.
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Object detection in optical remote sensing images: A survey and a new benchmark
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