Earthcube’s Recipe for Vehicle Detection

Earthcube’s Recipe for Vehicle Detection

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Data selection, training, architecture... Dive into the process of designing a deep learning model and get new advices with this handy guide, to develop a powerful vehicle detector on satellite images!

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CREDIT

Speaker - Julie Imbert

Script - Julie Imbert & Ségolène Husson
With the participation of the Earthcube team

Filmmaker, editing & motion design - Julien Mascheroni

ILLUSTRATIONS

Earthcube Proprietary Detections

Satellite Images
fMoW dataset, Functional Map of the World, CVPR, Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee, 2018
Maxar

Illustrations from Articles
5:44 Olaf Ronneberger, Philipp Fischer, Thomas Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015, arXiv,
6:19 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, 2015, arXiv,
6:25 Abhijit Guha Roy, Nassir Navab, Christian Wachinger, Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, 2018, arXiv,
8:14 Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson, Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs, 2018, arXiv,
8:15 Andrew Gordon Wilson, Averaging Weights Leads to Wider Optima and Better Generalization, 2018, arXiv.

ARTICLES

Yarin Gal, Riashat Islam, Zoubin Ghahramani, Deep Bayesian Active Learning with Image Data, 2017, arXiv,
Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon, CBAM: Convolutional Block Attention Module, 2018, arXiv,
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger, Snapshot Ensembles: Train 1, get M for free, 2017, arXiv,
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson, Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs, 2018, arXiv,
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, Piotr Dollár, Focal Loss for Dense Object Detection, 2017, arXiv,
Olaf Ronneberger, Philipp Fischer, Thomas Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015, arXiv,
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, 2015, arXiv,
Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton, Dynamic Routing Between Capsules, 2017, arXiv,
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, Pyramid Scene Parsing Network, 2016, arXiv,
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, Jian Sun, Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network, 2017, arXiv,
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Identity Mappings in Deep Residual Networks, 2016, arXiv,
Abhijit Guha Roy, Nassir Navab, Christian Wachinger, Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, 2018, arXiv,
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson, Averaging Weights Leads to Wider Optima and Better Generalization, 2018, arXiv.
Murat Seckin Ayhan, Philipp Berens, Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks, 2018,
Yarin Gal, Zoubin Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning, 2015.

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https://www.earthcube.eu
https://www.youtube.com/playlist?list=PLCqfmZWS97Q6QEJlF9zi2jzb7OIWN-eHz
https://medium.com/earthcube-stories

Видео Earthcube’s Recipe for Vehicle Detection канала Preligens