Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

Uddin, M.S.; Hoque, Reshad Ul; Islam, Kazi Aminul; Kwan, Chiman; Gribben, David; Li, Jiang

doi:10.3390/rs13163257

Cited by 18 publications

(15 citation statements)

References 53 publications

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“…Finally, ResNet-101 has been selected as a backbone network for the R-CNN models to detect animals in the training process. This selection of ResNet was also supported by the work of Kwan et al [33], as they achieved good performance with YOLO using ResNet. The main reason for that selection is the ability to balance between computational complexity and the animal species detection accuracy.…”

Section: Trainingmentioning

confidence: 70%

“…There are many attempts to identify animals by assigning a label to an image; however, there are limited works in the literature that focus on animal species detection, where the location of the animal is determined as well as its identification [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Some researchers used their own datasets which contain one or only a few animal species, and others used relatively small datasets (a few thousand images only) [28,31,32].…”

Section: Animal Species Detectionmentioning

confidence: 99%

“…This technique of cropping images allowed them to obtain 82% accuracy by using linear support vector machine (SVM) to classify 18 animal species. Kwan et al [32,33] used IR videos to classify and localize objects taken from different distances, and achieved a mean average precision 89.4% by using YOLO model. Chen et al [34] used 6-layers CNN to classify 20 animal species in their own dataset of 23,876 images with an accuracy of 38.32%.…”

Section: Animal Species Detectionmentioning

confidence: 99%

See 2 more Smart Citations

A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Ibraheam

Gebali

et al. 2021

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Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

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Section: Trainingmentioning

confidence: 70%

Section: Animal Species Detectionmentioning

confidence: 99%

Section: Animal Species Detectionmentioning

confidence: 99%

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A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Ibraheam

Gebali

et al. 2021

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“…Some studies expand the training samples by using artificially generated samples to deal with the small training data size problem. For example, several data generation methods based on generative adversarial network (GAN) models are proposed in [7][8][9][10] to generate realistic infrared images from optical images. Using data from the source domain to assist the interested target domain's task is also present in transfer learning.…”

Section: Introductionmentioning

confidence: 99%

SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size

Guo

Lyu

2021

Remote Sensing

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It is expensive and time-consuming to obtain a large number of labeled synthetic aperture radar (SAR) images. In the task of small training data size, the results of target detection on SAR images using deep network approaches are usually not ideal. In this study, considering that optical remote sensing images are much easier to be labeled than SAR images, we assume to have a large number of labeled optical remote sensing images and a small number of labeled SAR images with the similar scenes, propose to transfer knowledge from optical remote sensing images to SAR images, and develop a domain adaptive Faster R-CNN for SAR target detection with small training data size. In the proposed method, in order to make full use of the label information and realize more accurate domain adaptation knowledge transfer, an instance level domain adaptation constraint is used rather than feature level domain adaptation constraint. Specifically, generative adversarial network (GAN) constraint is applied as the domain adaptation constraint in the adaptation module after the proposals of Faster R-CNN to achieve instance level domain adaptation and learn the transferable features. The experimental results on the measured SAR image dataset show that the proposed method has higher detection accuracy in the task of SAR target detection with small training data size than the traditional Faster R-CNN.

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“…With the development of deep learning techniques, generative adversarial networks (GAN) present a new way to generate GPR images [27][28][29]. Nonetheless, training GAN is still a difficult issue in practice due to the instability of the GAN's learning process which is usually caused by the objective function [30,31]. Radford et al [31] raised a deep convolutional generative adversarial networks (DCGAN), which improves the convergence of the GAN and the quality of generated images.…”

Section: Introductionmentioning

confidence: 99%

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

et al. 2021

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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

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Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

Cited by 18 publications

References 53 publications

A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

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