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A-Faster R-CNN: Generating Hard Positive Examples via Adversary for Traffic Sign Detection Stephanie Milani and Christoph Mertz PROBLEM • Traffic signs must be well-maintained to keep roads safe. • Road infrastructure inventory and assessment systems that assist with traffic sign maintenance must be able to detect occluded traffic signs. • Problem: How do we robustly model invariances to rare occlusion events? • Gather more data? – No, too tedious and time-consuming. • Generate all possible occlusions? – No, impossible! • Combine the Faster R-CNN [3] architecture with the Adversarial Spatial Dropout Network proposed by Wang et al. [4] to create A-Faster R-CNN. • Train and test A-Faster R-CNN on the LISA dataset [5]. • Test A-Faster R-CNN on the Navlab dataset – specifically on occluded stop signs that were initially missed by the detector. • Evaluate the performance of the new network: Which cases is it still unable to classify? Which cases is it now able to classify? Network Design: The architecture of Faster R-CNN integrated with the Adversarial Spatial Dropout Network (ASDN). Given the input of region-based convolutional features proposed by the Region Proposal Network (RPN), the ASDN generates an occlusion mask to indicate which parts of the features to dropout. [1] A. Shrivastava, A. Gupta, and R. Girshick, "Training region-based object detectors with online hard example mining" in CVPR, 2017. [2] M. Takáč, A. Bijral, P. Richtárik, and N. Srebo, "Mini-batch primal and dual methods for svms" in Proceedings of the 30th International Conference on Machine Learning, vol. 28, 2013. [3] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks" in CVPR, 2015. [4] X. Wang, A. Shrivastava, and A. Gupta, "A-Fast-RCNN: Hard positive generation via adversary for object detection" in CVPR, 2017. [5] A. Møgelmose, M. Trevedi, and T. Moeslund, "Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey" in IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, 2012. ACKNOWLEDGMENTS FUTURE WORK • Train and test method on more traffic sign datasets. • Increase network robustness to sign discoloration and distortion by allowing adversary to further manipulate input. • Incorporate top-down methods, such as prior knowledge of sign locations. • Extend to real-time traffic sign detection. Thank you to Jina Wang, Dat Nguyen, the rest of the Navlab, and Nicholay Topin for their support. Special thanks to Traffic21 for sponsoring this work through the Women in Transportation Fellowship. SOLUTION • We do not have to generate all possible occlusions, just difficult ones [1] [2]. • Proposed Solution: Generate hard positive examples of occlusions using an adversary. • Goal of the detector: Accurately classify the sign in the image. • Goal of the adversary: Create examples of occluded signs that are good enough to trick the object detector into misclassifying the sign. • Integrate with network used by Navlab (Faster R-CNN) for their road infrastructure inventory and assessment system. METHODS WORKS CITED

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Page 1: A-Faster R-CNN: Generating Hard Positive Examples via ... · A-Faster R-CNN: Generating Hard Positive Examples via Adversary for Traffic Sign Detection Stephanie Milani and Christoph

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A-Faster R-CNN: Generating Hard Positive Examples via Adversary for Traffic Sign Detection

Stephanie Milani and Christoph Mertz

PROBLEM

• Traffic signs must be well-maintained to keep roads safe.

• Road infrastructure inventory and assessment systems that assist with traffic sign maintenance must be able to detect occluded traffic signs.

• Problem: How do we robustly model invariances to rare occlusion events?

• Gather more data? – No, too tedious and time-consuming.

• Generate all possible occlusions? – No, impossible!

• Combine the Faster R-CNN [3] architecture with the Adversarial Spatial Dropout Network proposed by Wang et al. [4] to create A-Faster R-CNN.

• Train and test A-Faster R-CNN on the LISA dataset [5].

• Test A-Faster R-CNN on the Navlab dataset – specifically on occluded stop signs that were initially missed by the detector.

• Evaluate the performance of the new network: Which cases is it still unable to classify? Which cases is it now able to classify?

Network Design: The architecture of Faster R-CNN integrated with the Adversarial Spatial Dropout Network (ASDN). Given the input of region-based convolutional features proposed by the Region Proposal Network (RPN), the ASDN generates an occlusion mask to indicate which parts of the features to dropout.

[1] A. Shrivastava, A. Gupta, and R. Girshick, "Training region-based object detectors with online hard example mining" in CVPR, 2017.

[2] M. Takáč, A. Bijral, P. Richtárik, and N. Srebo, "Mini-batch primal and dual methods for svms" in Proceedings of the 30th International Conference on Machine Learning, vol. 28, 2013.

[3] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks" in CVPR, 2015.

[4] X. Wang, A. Shrivastava, and A. Gupta, "A-Fast-RCNN: Hard positive generation via adversary for object detection" in CVPR, 2017.

[5] A. Møgelmose, M. Trevedi, and T. Moeslund, "Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey" in IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, 2012.

ACKNOWLEDGMENTSFUTURE WORK

• Train and test method on more traffic sign datasets.

• Increase network robustness to sign discoloration and distortion by allowing adversary to further manipulate input.

• Incorporate top-down methods, such as prior knowledge of sign locations.

• Extend to real-time traffic sign detection.

Thank you to Jina Wang, Dat Nguyen, the rest of the Navlab, and Nicholay Topin for their support. Special thanks to Traffic21 for sponsoring this work through the Women in Transportation Fellowship.

SOLUTION

• We do not have to generate all possible occlusions, just difficult ones [1] [2].

• Proposed Solution: Generate hard positive examples of occlusions using an adversary.• Goal of the detector: Accurately classify the sign in the

image.• Goal of the adversary: Create examples of occluded signs

that are good enough to trick the object detector into misclassifying the sign.

• Integrate with network used by Navlab (Faster R-CNN) for their road infrastructure inventory and assessment system.

METHODS WORKS CITED

Detection and Segmentation of Manufacturing Defects with ...eil.stanford.edu/publications/max_ferguson/SMSS_Casting_Defects.pdf · Mask R-CNN is an extension of Faster R-CNN that

Faster R-CNN: Towards Real-Time Object Detection … Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, and Jian

Face Detection with the Faster R-CNN - College of ...hzjiang/pubs/face_det_fg...Face Detection with the Faster R-CNN Huaizu Jiang University of Massachusetts Amherst Amherst MA 01003

Illuminating Pedestrians via Simultaneous Detection ...cvlab.cse.msu.edu/pdfs/Brazil_Yin_Liu_Illuminating_Pedestrians.pdf · pedestrian detection [21,26,29]. Faster R-CNN consists

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Weakly- and Semi-supervised Faster R-CNN with Curriculum ...xinggangw.info/pubs/wasscl-icpr18.pdf · very closing to the fully-supervised Faster R-CNN and its speed is as fast as

Few-Shot Adaptive Faster R-CNN...Few-shot Adaptive Faster R-CNN Tao Wang 1Xiaopeng Zhang,2 Li Yuan Jiashi Feng1 1Department of Electrical and Computer Engineering, National University

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Faster RER-CNN: application to the detection of vehicles in aerial images · 2018. 9. 21. · Faster RER-CNN: application to the detection of vehicles in aerial images Jean Ogier

Grid R-CNN · Mask R-CNN [11] extended Faster R-CNN by adding a branch for predicting an pixel-wise object mask. Differ-ent from Mask R-CNN, our method replaces the regression branch

Mask R-CNN - Facebook Research · PDF fileMask R-CNN is conceptually simple: Faster R-CNN has two outputs for each candidate object, a class label and a ... (RPN), proposes candidate

Meta R-CNN : Towards General Solver for Instance-level Low ... · inference in Faster /Mask R-CNNs. Each RoI feature refers to a single object or background, so Faster /Mask R-CNN

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CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN · that applied R-CNN and Faster R-CNN in medical imaging area. Our work complements the existing in demonstrating the application

IBM 과NVIDIA 가제안하는 딥러닝플랫폼 · 2017-07-21 · IBM 과NVIDIA 가제안하는 딥러닝플랫폼 ˹˹ 개요 Faster R-CNN 1. CNN 란? 2. R-CNN 란? 3. Faster R-CNN

A Faster R-CNN Approach for Extracting Indoor Navigation ... · A Faster R-CNN Approach for Extracting Indoor Navigation Graph from Building Designs L. Niu1, *, Y.Q Song2 1 School

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Siam R-CNN: Visual Tracking by Re-Detectionopenaccess.thecvf.com/content_CVPR_2020/papers/... · re-detector, Siam R-CNN, an adaptation of Faster R-CNN [54] with a Siamese architecture,

Chapter Multi-Person Tracking Based on Faster R-CNN and

Faster R-CNN Features for Instance Searchopenaccess.thecvf.com/.../w12/...Faster_R-CNN_Features_CVPR_201… · Faster R-CNN, the RPN shares features with the object de-tection network

Mask r-cnn · 2018-02-09 · Overview of Mask R-CNN •Goal: to create a framework for Instance segmentation •Builds on top of Faster R-CNN by adding a parallel branch •For each

Mask R-CNN - cseweb.ucsd.edu · Faster R-CNN: Fast R-CNN + RPN Region Proposal Network (RPN) after last convolutional layer RPN produces region proposals directly Can be extended

Faster R-CNN Features for Instance Search · 2016-05-30 · Faster R-CNN Features for Instance Search Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marqu´ es´ Universitat Politecnica

Face Detection with the Faster R-CNN - arXiv · Face Detection with the Faster R-CNN Huaizu Jiang University of Massachusetts Amherst Amherst MA 01003 [email protected] Erik Learned-Miller

Faster R-CNN with Region Proposal Refinementcs231n.stanford.edu/reports/2017/pdfs/112.pdf · Faster R-CNN with Region Proposal Refinement Peng Yuan Electrical Engineering Department

Rethinking the Faster R-CNN Architecture for …...this paradigm by first generating segment proposals from the full video, followed by classifying each proposal. Among region-based

Mask R-CNN ICCV 2017(Oral)imlab.postech.ac.kr/dkim/class/csed703G_2019f/maskrcn.pdf · 2019-11-20 · Mask R-CNN extends Faster R-CNN by adding a branch for predicting an object mask

Image-Based Food Calorie Estimation Using Knowledge on ...img.cs.uec.ac.jp/pub/conf17/171223ege_3_ppt.pdf · Faster R-CNNの導入 S. Ren et al. Faster R-CNN: Towards realtime object

Deep Learning for Image Instance Segmentation ----Mask R-CNN · object instance segmentation. •Mask R-CNN, extends Faster R-CNN by adding a branch for predicting segmentation masks

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6937153.pdf · 3.3.2 Mask RCNN Mask R-CNN is an extension of the Faster RCNN model [2]. Faster R-CNN is a Region Proposal

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