scholarly journals Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

2019 ◽  
Author(s):  
Ranjith Dinakaran ◽  
Philip Easom ◽  
Li Zhang ◽  
Ahmed Bouridane ◽  
Richard Jiang ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
Adversarial Networks ◽  
In The Wild

Intrusion detection of railway clearance from infrared images using generative adversarial networks

2020 ◽  
pp. 1-13
Author(s):  
Yundong Li ◽  
Yi Liu ◽  
Han Dong ◽  
Wei Hu ◽  
Chen Lin
Keyword(s):  
Intrusion Detection ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Single Shot ◽  
Data Generation ◽  
Infrared Images ◽  
Adversarial Networks ◽  
Training Samples ◽  
Rgb Images

The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime.

scholarly journals Age Progression and Regression with Spatial Attention Modules

2020 ◽  
Vol 34 (07) ◽  
pp. 11378-11385
Author(s):  
Qi Li ◽  
Yunfan Liu ◽  
Zhenan Sun
Keyword(s):  
Spatial Attention ◽  
Generative Adversarial Networks ◽  
Age Progression ◽  
Training Images ◽  
Adversarial Networks ◽  
Face Images ◽  
Multiple Datasets ◽  
Image Translation ◽  
In The Wild ◽  
Face Aging

Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.

scholarly journals FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 807 ◽  
Author(s):  
Weiwei Zhuang ◽  
Liang Chen ◽  
Chaoqun Hong ◽  
Yuxin Liang ◽  
Keshou Wu
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Adversarial Networks ◽  
Key Points ◽  
In The Wild ◽  
Novel Method ◽  
Face Synthesis ◽  
Unconstrained Face

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN.

scholarly journals Weakly Supervised GAN for Image-to-Image Translation in the Wild

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhiyi Cao ◽  
Shaozhang Niu ◽  
Jiwei Zhang
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Translation ◽  
Latent Space ◽  
In The Wild ◽  
Adversarial Examples ◽  
Weakly Supervised ◽  
Public Datasets ◽  
Global And Local ◽  
The Given

Generative Adversarial Networks (GANs) have achieved significant success in unsupervised image-to-image translation between given categories (e.g., zebras to horses). Previous GANs models assume that the shared latent space between different categories will be captured from the given categories. Unfortunately, besides the well-designed datasets from given categories, many examples come from different wild categories (e.g., cats to dogs) holding special shapes and sizes (short for adversarial examples), so the shared latent space is troublesome to capture, and it will cause the collapse of these models. For this problem, we assume the shared latent space can be classified as global and local and design a weakly supervised Similar GANs (Sim-GAN) to capture the local shared latent space rather than the global shared latent space. For the well-designed datasets, the local shared latent space is close to the global shared latent space. For the wild datasets, we will get the local shared latent space to stop the model from collapse. Experiments on four public datasets show that our model significantly outperforms state-of-the-art baseline methods.

scholarly journals FRGAN: A Blind Face Restoration with Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Zhifeng Chen ◽  
Jinjin Liu
Keyword(s):  
Activation Function ◽  
Human Motion ◽  
Face Image ◽  
Head Pose Estimation ◽  
Generative Adversarial Networks ◽  
3D Face Reconstruction ◽  
Adversarial Networks ◽  
The Face ◽  
In The Wild ◽  
Facial Images

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

scholarly journals KNOWLEDGE DISTILLATION USING GANS FOR FAST OBJECT DETECTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 583-588
Author(s):  
E. Finogeev ◽  
V. Gorbatsevich ◽  
A. Moiseenko ◽  
Y. Vizilter ◽  
O. Vygolov
Keyword(s):  
Object Detection ◽  
New Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
Detection Algorithms ◽  
Adversarial Networks ◽  
Knowledge Distillation ◽  
Baseline Algorithm

Abstract. In this paper, we propose a new method for knowledge distilling based on generative adversarial networks. Discriminator CNNs is used as an adaptive knowledge distilling loss. In experiments, single shot multibox detector SSD based on MobileNet v2 and ShuffleNet v1 are used as student networks. Our tests showed AP and mAP improvement of more than 3% on PascalVOC and 1% on MS Coco datasets compared with the baseline algorithm without any architecture or dataset changes. The proposed approach is general and can be used not only with SSD but also with any type of object detection algorithms.

scholarly journals Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4430 ◽  
Author(s):  
Ze Luo ◽  
Huiling Yu ◽  
Yizhuo Zhang
Keyword(s):  
Data Augmentation ◽  
Pine Forests ◽  
Generative Adversarial Networks ◽  
Detection Accuracy ◽  
Single Shot ◽  
Korean Pine ◽  
Adversarial Networks ◽  
Pine Cone ◽  
Boundary Equilibrium ◽  
Pine Cones

The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a certain number of detection accuracy for image processing of fruits in trees using deep-learning methods, but the overall performance of these methods has not been satisfactory, and they have never been used in the detection of pine cones. In this paper, a pine cone detection method based on Boundary Equilibrium Generative Adversarial Networks (BEGAN) and You Only Look Once (YOLO) v3 mode is proposed to solve the problems of insufficient data set, inaccurate detection result and slow detection speed. First, we use traditional image augmentation technology and generative adversarial network BEGAN to implement data augmentation. Second, we introduced a densely connected network (DenseNet) structure in the backbone network of YOLOv3. Third, we expanded the detection scale of YOLOv3, and optimized the loss function of YOLOv3 using the Distance-IoU (DIoU) algorithm. Finally, we conducted a comparative experiment. The experimental results show that the performance of the model can be effectively improved by using BEGAN for data augmentation. Under same conditions, the improved YOLOv3 model is better than the Single Shot MultiBox Detector (SSD), the faster-regions with convolutional neural network (Faster R-CNN) and the original YOLOv3 model. The detection accuracy reaches 95.3%, and the detection efficiency is 37.8% higher than that of the original YOLOv3.

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