scholarly journals A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1490
Author(s):  
Yan Liu ◽  
Tiantian Qiu ◽  
Jingwen Wang ◽  
Wenting Qi
Keyword(s):  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Vital Role ◽  
Detection Accuracy ◽  
Local Regression ◽  
Generative Adversarial Network ◽  
Automatic Driving ◽  
Road Lighting ◽  
Adversarial Network

Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Multi-omics Data Integration by Generative Adversarial Network

2021 ◽  
Author(s):  
Khandakar Tanvir Ahmed ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
Wei Zhang
Keyword(s):  
Large Scale ◽  
Synthetic Data ◽  
Interaction Network ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Omics Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cancer Outcome

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network (GAN) model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Large-scale experiments on The Cancer Genome Atlas (TCGA) breast cancer and ovarian cancer datasets validate that (1) the model can effectively integrate two omics data (i.e., mRNA and microRNA expression data) and their interaction network (i.e., microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (2) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.

scholarly journals Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1089 ◽  
Author(s):  
Ye Wang ◽  
Zhenyi Liu ◽  
Weiwen Deng
Keyword(s):  
Pedestrian Detection ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Fixed Size ◽  
Feature Maps ◽  
Feature Map ◽  
Bounding Box ◽  
New Feature ◽  
And Training

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.

scholarly journals AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5479 ◽  
Author(s):  
Maryam Rahnemoonfar ◽  
Jimmy Johnson ◽  
John Paden
Keyword(s):  
Data Augmentation ◽  
Synthetic Data ◽  
Detection Algorithm ◽  
Contour Detection ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Radar Images ◽  
Adversarial Network ◽  
Radar Imagery

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

scholarly journals Multimodal Structure-Consistent Image-to-Image Translation

2020 ◽  
Vol 34 (07) ◽  
pp. 11490-11498
Author(s):  
Che-Tsung Lin ◽  
Yen-Yi Wu ◽  
Po-Hao Hsu ◽  
Shang-Hong Lai
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Detection Accuracy ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Image Objects ◽  
Perceptual Distance ◽  
Adversarial Network ◽  
Image Translation ◽  
Realistic Images

Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.

scholarly journals Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 286
Author(s):  
Younis Abdalla ◽  
M. Tariq Iqbal ◽  
Mohamed Shehata
Keyword(s):  
Neural Network ◽  
New Technologies ◽  
Generative Adversarial Networks ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Generative Adversarial Network ◽  
Neural Network Approach ◽  
Adversarial Network ◽  
Copy Move Forgery Detection ◽  
Detection And Localization

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

Vehicle Detection Based on Binocular Stereovision and Edge Extraction

2012 ◽  
Vol 190-191 ◽  
pp. 1090-1093
Author(s):  
Fei Tong ◽  
Lei Xu
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accident ◽  
High Efficiency ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Transportation Systems ◽  
Vehicle Safety ◽  
Edge Extraction ◽  
Binocular Stereovision

Traffic accident has already become a significant social problem. Intelligent Transportation Systems (ITS) is vital to improve the vehicle safety. As an important part of ITS, vehicle detection has been widespread concern. This paper presents a vehicle detection algorithm based on binocular stereovision and edge extraction: analyze spatial coordinate feature belong to object point and detect obstacle points; segment Region of Interest; apply symmetry to detect vehicle. The experimental results indicate that the algorithm possess high efficiency and accuracy.

Realtime On-Road Vehicle Detection Approach Based on Cascaded Structure

2011 ◽  
Vol 130-134 ◽  
pp. 2429-2432
Author(s):  
Liang Xiu Zhang ◽  
Xu Yun Qiu ◽  
Zhu Lin Zhang ◽  
Yu Lin Wang
Keyword(s):  
Warning System ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Assistance ◽  
Road Vehicle ◽  
Collision Warning ◽  
Detection Approach ◽  
Transportation Applications

Realtime on-road vehicle detection is a key technology in many transportation applications, such as driver assistance, autonomous driving and active safety. A vehicle detection algorithm based on cascaded structure is introduced. Haar-like features are used to built model in this application, and GAB algorithm is chosen to train the strong classifiers. Then, the real-time on-road vehicle classifier based on cascaded structure is constructed by combining the strong classifiers. Experimental results show that the cascaded classifier is excellent in both detection accuracy and computational efficiency, which ensures its application to collision warning system.

scholarly journals An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1358
Author(s):  
Yan Liu ◽  
Jingwen Wang ◽  
Tiantian Qiu ◽  
Wenting Qi
Keyword(s):  
High Speed ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Input Image ◽  
Research Field ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Intelligent Traffic System ◽  
Blurred Image ◽  
Blurred Images

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

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