Orientation-Aware Vehicle Detection in Aerial Images via an Anchor-Free Object Detection Approach

2021 ◽  
pp. 1-13
Author(s):  
Furong Shi ◽  
Tong Zhang ◽  
Tao Zhang
Keyword(s):  
Object Detection ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Free Object ◽  
Detection Approach

scholarly journals DAR-Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Kaifeng Li ◽  
Bin Wang
Keyword(s):  
Object Detection ◽  
Information Flow ◽  
Spatial Information ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Feature Maps ◽  
Residual Network ◽  
General Object ◽  
The Impact

With the rapid development of deep learning and the wide usage of Unmanned Aerial Vehicles (UAVs), CNN-based algorithms of vehicle detection in aerial images have been widely studied in the past several years. As a downstream task of the general object detection, there are some differences between the vehicle detection in aerial images and the general object detection in ground view images, e.g., larger image areas, smaller target sizes, and more complex background. In this paper, to improve the performance of this task, a Dense Attentional Residual Network (DAR-Net) is proposed. The proposed network employs a novel dense waterfall residual block (DW res-block) to effectively preserve the spatial information and extract high-level semantic information at the same time. A multiscale receptive field attention (MRFA) module is also designed to select the informative feature from the feature maps and enhance the ability of multiscale perception. Based on the DW res-block and MRFA module, to protect the spatial information, the proposed framework adopts a new backbone that only downsamples the feature map 3 times; i.e., the total downsampling ratio of the proposed backbone is 8. These designs could alleviate the degradation problem, improve the information flow, and strengthen the feature reuse. In addition, deep-projection units are used to reduce the impact of information loss caused by downsampling operations, and the identity mapping is applied to each stage of the proposed backbone to further improve the information flow. The proposed DAR-Net is evaluated on VEDAI, UCAS-AOD, and DOTA datasets. The experimental results demonstrate that the proposed framework outperforms other state-of-the-art algorithms.

scholarly journals A Deep-Learning-Based Vehicle Detection Approach for Insufficient and Nighttime Illumination Conditions

2019 ◽  
Vol 9 (22) ◽  
pp. 4769 ◽  
Author(s):  
Ho Kwan Leung ◽  
Xiu-Zhi Chen ◽  
Chao-Wei Yu ◽  
Hong-Yi Liang ◽  
Jian-Yi Wu ◽  
...  
Keyword(s):  
Object Detection ◽  
Vehicle Detection ◽  
Fixed Number ◽  
Detection Performance ◽  
Data Sets ◽  
Detection Techniques ◽  
Ambient Lighting ◽  
Occluded Objects ◽  
Detection Approach ◽  
Public Data

Most object detection models cannot achieve satisfactory performance under nighttime and other insufficient illumination conditions, which may be due to the collection of data sets and typical labeling conventions. Public data sets collected for object detection are usually photographed with sufficient ambient lighting. However, their labeling conventions typically focus on clear objects and ignore blurry and occluded objects. Consequently, the detection performance levels of traditional vehicle detection techniques are limited in nighttime environments without sufficient illumination. When objects occupy a small number of pixels and the existence of crucial features is infrequent, traditional convolutional neural networks (CNNs) may suffer from serious information loss due to the fixed number of convolutional operations. This study presents solutions for data collection and the labeling convention of nighttime data to handle various types of situations, including in-vehicle detection. Moreover, the study proposes a specifically optimized system based on the Faster region-based CNN model. The system has a processing speed of 16 frames per second for 500 × 375-pixel images, and it achieved a mean average precision (mAP) of 0.8497 in our validation segment involving urban nighttime and extremely inadequate lighting conditions. The experimental results demonstrated that our proposed methods can achieve high detection performance in various nighttime environments, such as urban nighttime conditions with insufficient illumination, and extremely dark conditions with nearly no lighting. The proposed system outperforms original methods that have an mAP value of approximately 0.2.

scholarly journals Vehicle detection using morphological shared-weight neural network in the multiple instance learning framework

2017 ◽  
Author(s):  
◽  
Anes Ouadou
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Supervised Learning ◽  
Vehicle Detection ◽  
Multiple Instance Learning ◽  
Aerial Images ◽  
Learning Framework

In this thesis, we design and implement an algorithm for object detection in aerial images based on the morphological shared-weight neural network (MSNN). The multiple instance learning (MIL) framework is used to avoid the labeling problem required in a supervised learning framework. Using the MIL, each image was given a single label. We rely on the MSNN's ability to detect objects, and on the methodology used to generate bags to find our target. Two multiple-instance MSNN structures are developed. The performance of this framework is compared with the performance of a convolutional neural network (CNN) in the same condition.

Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images

2020 ◽  
pp. 1-5
Author(s):  
Kun Ding ◽  
Guojin He ◽  
Huxiang Gu ◽  
Zisha Zhong ◽  
Shiming Xiang ◽  
...  
Keyword(s):  
Object Detection ◽  
Aerial Images

Tiny Object Detection in Aerial Images

2021 ◽  
Author(s):  
Jinwang Wang ◽  
Wen Yang ◽  
Haowen Guo ◽  
Ruixiang Zhang ◽  
Gui-Song Xia
Keyword(s):  
Object Detection ◽  
Aerial Images

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

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