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README.md

@@ -55,6 +55,7 @@ If you know of a paper that addresses an Vision-based Small Object Detection pro
 - Multiple receptive fields and small-object-focusing weaklysupervised segmentation network for fast object detection, arXiv 2019, [[paper]](https://arxiv.org/ftp/arxiv/papers/1904/1904.12619.pdf)
 - A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles, Complexity 2019, [[paper]](http://downloads.hindawi.com/journals/complexity/2019/4042624.pdf)
 - Small Object Sensitive Segmentation of Urban Street Scene with Spatial Adjacency Between Object Classes, IEEE TIP 2019, [[paper]](https://cse.sc.edu/~songwang/document/tip19b.pdf)
+- Small traffic sign detection from large image, Springer Applied Intelligence 2019, [[paper]](https://link.springer.com/article/10.1007%2Fs10489-019-01511-7)
 ## 1.2. Connection method of different feature maps <a name="1.2"></a>  
 - Detecting Small Objects Using a Channel-Aware Deconvolutional Network, IEEE TCSVT 2019, [[paper]](https://ieeexplore.ieee.org/document/8669953) 
 - A unified multi-scale deep convolutional neural network for fast object detection, ECCV 2016, [[paper]](http://www.cvlibs.net/projects/autonomous_vision_survey/literature/Cai2016ECCV.pdf)
@@ -71,17 +72,20 @@ If you know of a paper that addresses an Vision-based Small Object Detection pro
 - SCAN: Semantic Context Aware Network for Accurate Small Object Detection, IJCIS 2018, [[paper]](https://www.atlantis-press.com/journals/ijcis/25894607/view)
 - SINet: A ScaleInsensitive Convolutional Neural Network for Fast Vehicle Detection, IEEE ITS 2019, [[paper]](https://arxiv.org/pdf/1804.00433.pdf)
 - Robust Obstacle Detection and Recognition for Driver Assistance Systems, IEEE ITS 2019, [[paper]](https://www.researchgate.net/profile/Jiaxu_Leng2/publication/332438373_Robust_Obstacle_Detection_and_Recognition_for_Driver_Assistance_Systems/links/5cbeb092a6fdcc1d49a87438/Robust-Obstacle-Detection-and-Recognition-for-Driver-Assistance-Systems.pdf)
--  Object Detection in Aerial Images Using Feature Fusion Deep Networks, IEEE Acess 2019, [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8661761)
+- Object Detection in Aerial Images Using Feature Fusion Deep Networks, IEEE Acess 2019, [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8661761)
 - Small Object Detection in Unmanned Aerial Vehicle Images Using Feature Fusion and Scaling-Based Single Shot Detector with Spatial Context Analysis, IEEE TCSVT 2019, [[paper]](https://ieeexplore.ieee.org/abstract/document/8672115) 
+- Small Object Detection Using Context Information Fusion in Faster R-CNN, IEEE ICCC 2018, [[paper]](https://ieeexplore.ieee.org/abstract/document/8780579)
 
 # 3. Super-Resolution <a name="3"></a>
 - JCS-Net: Joint Classification and SuperResolution Network for Small-scale Pedestrian Detection in Surveillance Images, IEEE TIFS 2019, [[paper]](https://ieeexplore.ieee.org/abstract/document/8714071) 
 - Accurate image superresolution using very deep convolutional networks, CVPR 2016, [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Kim_Accurate_Image_Super-Resolution_CVPR_2016_paper.pdf)
 - Finding tiny faces in the wild with generative adversarial network, CVPR 2018, [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bai_Finding_Tiny_Faces_CVPR_2018_paper.pdf)
 - Multi-branch fully convolutional network for face detection, arXiv 2017, [[paper]](https://arxiv.org/pdf/1707.06330.pdf)
 - SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network, ECCV 2018, [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.pdf)
--  Improving Small Object Detection, ACPR 2017, [[paper]](http://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/ConferencePapers/2017/Improving-SmallObject-Detection.pdf)
+- Improving Small Object Detection, ACPR 2017, [[paper]](http://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/ConferencePapers/2017/Improving-SmallObject-Detection.pdf)
 - Perceptual Generative Adversarial Networks for Small Object Detection, CVPR 2017, [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf)
+- Dynamic Up-Sampling Network for Small Airplane Landing Gear Detection, IEEE Access, [[paper]](https://ieeexplore.ieee.org/abstract/document/8766797)
+- Prior Knowledge Guided Small Object Detection on High-Resolution Images, IEEE ICIP 2019, [[paper]](https://ieeexplore.ieee.org/abstract/document/8802612)
 
 # 4. Region-Proposal <a name="4"></a>
 - STDnet: A ConvNet for Small Target Detection, BMVC 2018, [[paper]](http://bmvc2018.org/contents/papers/0897.pdf)
@@ -93,6 +97,7 @@ If you know of a paper that addresses an Vision-based Small Object Detection pro
 - Cascade Mask Generation Framework for Fast Small Object Detection, IEEE ICME 2018, [[paper]](https://ieeexplore.ieee.org/abstract/document/8486561) 
 - Cascaded CNN Method for Far Object Detection in Outdoor Surveillance, IEEE SITIS 2018, [[paper]](https://ieeexplore.ieee.org/abstract/document/8706233) 
 - SSD-MSN: An Improved Multi-scale Object Detection Network Based on SSD, IEEE Access 2019, [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8736726)
+- Efficient Small Object Detection with an Improved Region Proposal Networks, IOP Conf. Ser.: Mater. Sci. Eng. 2019, [[paper]](https://iopscience.iop.org/article/10.1088/1757-899X/533/1/012062)
 
 # 5. Others <a name="5"></a>
 
@@ -104,13 +109,17 @@ If you know of a paper that addresses an Vision-based Small Object Detection pro
 - An Efficient Color Space for Deep-Learning Based Traffic Light Recognition, J Adv Transport 2018, [[paper]](http://downloads.hindawi.com/journals/jat/2018/2365414.pdf)
 - CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving, arXiv 2018, [[paper]](https://arxiv.org/pdf/1806.09790.pdf)
 - Research on Vehicle Object Detection Method Based on Convolutional Neural Network, IEEE ISCID 2018, [[paper]](https://ieeexplore.ieee.org/abstract/document/8695461) 
+- An Improved Faster R-CNN for Small Object Detection, IEEE Access 2019, [[paper]](https://ieeexplore.ieee.org/document/8786135/authors#authors)
+- Detecting Small Objects in Urban Settings Using SlimNet Model, IEEE TGRS 2019, [[paper]](https://ieeexplore.ieee.org/abstract/document/8746784)
+- Smaller Object Detection for Real-Time Embedded Traffic Flow Estimation Using Fish-Eye Cameras, IEEE ICIP 2019, [[paper]](https://ieeexplore.ieee.org/abstract/document/8803719)
 
 ## 5.2. Small object detection in aerial image <a name="5.2"></a> 
 - Clustered Object Detection in Aerial Images, arXiv 2019, [[paper]](https://arxiv.org/pdf/1904.08008.pdf)
 - Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning, IEEE ICIP 2018, [[paper]](https://ieeexplore.ieee.org/abstract/document/8451144) 
--  Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation, arXiv 2019, [[paper]](https://arxiv.org/ftp/arxiv/papers/1904/1904.11619.pdf)
+- Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation, arXiv 2019, [[paper]](https://arxiv.org/ftp/arxiv/papers/1904/1904.11619.pdf)
 - Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects, arXiv 2017, [[paper]](https://arxiv.org/pdf/1709.04666.pdf)
 - Combining deep features for object detection at various scales: finding small birds in landscape images, IPSJ CVA 2016, [[paper]](https://ipsjcva.springeropen.com/track/pdf/10.1186/s41074-016-0006-z)
+- The Power of Tiling for Small Object Detection, CVPR 2019, [[paper]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/UAVision/Unel_The_Power_of_Tiling_for_Small_Object_Detection_CVPRW_2019_paper.pdf)
 
 ## 5.3. others <a name="5.3"></a> 
 - An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection, CVPR 2019, [[paper]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/CEFRL/Lee_An_Energy_and_GPU-Computation_Efficient_Backbone_Network_for_Real-Time_Object_CVPRW_2019_paper.pdf)