Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

<p><strong>Abstract.</strong> Recent advances in machine learning techniques for image classification have led to the development of robust approaches to both object detection and extraction. Traditional CNN architectures, such as LeNet, AlexNet and CaffeNet, usually use as input images of fixed sizes taken from objects and attempt to assign labels to those images. Another possible approach is the Fast Region-based CNN (or Fast R-CNN), which works by using two models: (i) a Region Proposal Network (RPN) which generates a set of potential Regions of Interest (RoI) in the image; and (ii) a traditional CNN which assigns labels to the proposed RoI. As an alternative, this study proposes an approach to automatic object extraction from aerial images similar to the Fast R-CNN architecture, the main difference being the use of the Simple Linear Iterative Clustering (SLIC) algorithm instead of an RPN to generate the RoI. The dataset used is composed of high-resolution aerial images and the following classes were considered: house, sport court, hangar, building, swimming pool, tree, and street/road. The proposed method can generate RoI with different sizes by running a multi-scale SLIC approach. The overall accuracy obtained for object detection was 89% and the major advantage is that the proposed method is capable of semantic segmentation by assigning a label to each selected RoI. Some of the problems encountered are related to object proximity, in which different instances appeared merged in the results.</p>

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Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images

Remote Sensing ◽

10.3390/rs12111760 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1760 ◽

Cited By ~ 2

Author(s):

Wang Zhang ◽

Chunsheng Liu ◽

Faliang Chang ◽

Ye Song

Keyword(s):

Feature Fusion ◽

Vehicle Detection ◽

Aerial Images ◽

Feature Maps ◽

Vehicle Monitoring ◽

Multi Scale ◽

Adaptive Fusion ◽

Feature Pyramid ◽

Vehicle Information ◽

Vehicle Segmentation

With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works.

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The relation between accuracy and size of structure element for vehicle detection with high resolution highway aerial images

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6723366 ◽

2013 ◽

Cited By ~ 4

Author(s):

Shang Li ◽

Guoqing Zhou ◽

Zezhong Zheng ◽

Yalan Liu ◽

Xiaowen Li ◽

...

Keyword(s):

High Resolution ◽

Vehicle Detection ◽

Aerial Images

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Context Aggregation Network for Semantic Labeling in Aerial Images

Remote Sensing ◽

10.3390/rs11101158 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1158 ◽

Cited By ~ 11

Author(s):

Wensheng Cheng ◽

Wen Yang ◽

Min Wang ◽

Gang Wang ◽

Jinyong Chen

Keyword(s):

High Resolution ◽

Multiple Scales ◽

Image Data ◽

Remote Sensing Image ◽

Object Localization ◽

Aerial Images ◽

Localization Accuracy ◽

Multi Scale ◽

Semantic Labeling ◽

Multi Level

Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data.

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Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2015.2451002 ◽

2016 ◽

Vol 54 (1) ◽

pp. 103-116 ◽

Cited By ~ 52

Author(s):

Ziyi Chen ◽

Cheng Wang ◽

Chenglu Wen ◽

Xiuhua Teng ◽

Yiping Chen ◽

...

Keyword(s):

High Resolution ◽

Sparse Representation ◽

Vehicle Detection ◽

Aerial Images

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Vehicle detection from high-resolution aerial images based on superpixel and color name features

10.1117/12.2234762 ◽

2016 ◽

Author(s):

Ziyi Chen ◽

Liujuan Cao ◽

Zang Yu ◽

Yiping Chen ◽

Cheng Wang ◽

...

Keyword(s):

High Resolution ◽

Vehicle Detection ◽

Aerial Images ◽

Color Name

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Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images

Remote Sensing ◽

10.3390/rs13040692 ◽

2021 ◽

Vol 13 (4) ◽

pp. 692

Author(s):

Yuwei Jin ◽

Wenbo Xu ◽

Ce Zhang ◽

Xin Luo ◽

Haitao Jia

Keyword(s):

High Resolution ◽

Large Scale ◽

Aerial Images ◽

Data Sets ◽

Building Extraction ◽

Visual Interpretation ◽

Urban Scenes ◽

Multi Scale ◽

Spatial Pyramid Pooling ◽

Very High

Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations.

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