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R-FCN: Object Detection via Region-based Fully Convolutional Networks
TL;DR: This work presents region-based, fully convolutional networks for accurate and efficient object detection, and proposes position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.
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Abstract: We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets), for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20x faster than the Faster R-CNN counterpart. Code is made publicly available at: this https URL
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Citations
Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes
TL;DR: This paper proposes a semi-supervised large scale fine-grained detection method, which only needs bounding box annotations of a smaller number of coarse- grained classes and image-level labels of large scalefine-grains classes, and can detect all classes at nearly fully-super supervised accuracy.
Every Feature Counts: An Improved One-Stage Detector in Thermal Imagery
Yu Cao,Tong Zhou,Xinhua Zhu,Yan Su +3 more
- 01 Dec 2019
TL;DR: This work proposes an DNN-based, one-stage detector namely ThermalDet, which inherits the architecture of RefineDet and further improves it and demonstrates that ThermalDet performs better than the state-of-art methods such as MMTOD-UNIT, M MTOD-CG.
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A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets
TL;DR: In this article , the authors surveyed and compared publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently presented a systematic and comprehensive review of existing works that employ such datasets.
CCPrune: Collaborative channel pruning for learning compact convolutional networks
TL;DR: Wang et al. as mentioned in this paper proposed a method called collaborative channel pruning (CCPrune) to evaluate the importance of channels, which combines the convolution layer weights and the BN layer scaling factors.
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Automatic Multiclass Instance Segmentation of Concrete Damage Using Deep Learning Model
TL;DR: In this article, Mask Region-Based Convolutional Neural Network (Mask R-CNN) was used to detect and segment defects in civil infrastructure with multiple objects, such as Horizontal Crack, Vertical Crack, Diagonal Crack, Branch Crack, and Spall.
References
•Proceedings Article
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Liang-Chieh Chen,George Papandreou,Iasonas Kokkinos,Kevin Murphy,Alan L. Yuille +4 more
- 07 May 2015
TL;DR: DeepLab as mentioned in this paper combines the responses at the final layer with a fully connected CRF to localize segment boundaries at a level of accuracy beyond previous methods, achieving 71.6% IOU accuracy in the test set.
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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Sean Bell,C. Lawrence Zitnick,Kavita Bala,Ross Girshick +3 more
- 01 Jun 2016
TL;DR: The Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest, provides strong evidence that context and multi-scale representations improve small object detection.
•Posted Content
Instance-sensitive Fully Convolutional Networks
TL;DR: This paper develops FCNs that are capable of proposing instance-level segment candidates that do not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances.
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