A novel data augmentation approach for mask detection using deep transfer learning

Object detection is a technology that deals with recognizing classes of objects and their location. It is used in many different areas, such as in face-detecting systems [16, 34, 37], surveillance tools [9], human-machine interfaces [17], and self-driving cars [18, 23, 25, 26, 30]. These days, deep learning object detection approaches have achieved significantly better performance than the classical feature-based algorithms. Darknet [31] is a deep learning object detection framework, which is well known for its fast speed and simple structure. Unfortunately, Darknet can only work with Nvidia CUDA [6] for accelerating its deep learning calculations. For this reason, users have only limited options of selecting appropriate graphic cards. Open computing language (OpenCL) [35], an open standard for cross-platform, parallel programming of heterogeneous systems, is available for the general hardware accelerators. However, many deep learning frameworks including Darknet have no support for OpenCL. In our previous paper, we presented OpenCL-Darknet [19], which transformed the CUDA-based Darknet into an open standard OpenCL backend. The original OpenCL-Darknet successfully showed its ability for the general graphics processing unit (GPU) hardware. However, it could not achieve competitive performance compared with the CUDA version, and it only supported a limited platform. In this study, we improved the performance of OpenCL-Darknet with several optimization techniques and added support for various architectures. We also evaluated OpenCL-Darknet not only in AMD R7 accelerated processing unit (APU) with OpenCL 2.0, but also in Nvidia GPU and ARM Mali embedded GPU with OpenCL 1.2 Profile. The evaluation using the standard object detection datasets showed that our advanced OpenCL-Darknet reduced the processing time by at most 50% on average for various deep learning object detection networks compared with our original implementation. We also showed that our OpenCL deep learning framework has competitiveness compared with the CUDA-based one.

Much research on object detection focuses on building better model architectures and detection algorithms. Changing the model architecture, however, comes at the cost of adding more complexity to inference, making models slower. Data augmentation, on the other hand, doesn’t add any inference complexity, but is insufficiently studied in object detection for two reasons. First it is more difficult to design plausible augmentation strategies for object detection than for classification, because one must handle the complexity of bounding boxes if geometric transformations are applied. Secondly, data augmentation attracts less research attention perhaps because it is believed to add less value and to transfer poorly compared to advances in network architectures.This paper serves two main purposes. First, we propose to use AutoAugment [3] to design better data augmentation strategies for object detection because it can address the difficulty of designing them. Second, we use the method to assess the value of data augmentation in object detection and compare it against the value of architectures. Our investigation into data augmentation for object detection identifies two surprising results. First, by changing the data augmentation strategy to our method, AutoAugment for detection, we can improve RetinaNet with a ResNet-50 backbone from 36.7 to 39.0 mAP on COCO, a difference of +2.3 mAP. This gain exceeds the gain achieved by switching the backbone from ResNet-50 to ResNet-101 (+2.1 mAP), which incurs additional training and inference costs. The second surprising finding is that our strategies found on the COCO dataset transfer well to the PASCAL dataset to improve accuracy by +2.7 mAP. These results together with our systematic studies of data augmentation call into question previous assumptions about the role and transferability of architectures versus data augmentation. In particular, changing the augmentation may lead to performance gains that are equally transferable as changing the underlying architecture.

Since the last 3 years, Pakistan has been focusing considerably on the increase in tree plantation in several areas throughout the country. With this increase in plantation, the need for up-to-date record keeping for upkeep of these trees across the country arises. The extensive research in object detection and image segmentation models have led to a much faster method of satellite image based tree counting to replace conventional counting methods. This paper focuses on tree detection and counting using satellite images, spanning a total of 8 years, of a university campus located in the capital of Pakistan. It effectively makes use of data augmentation techniques to improve the accuracy of the implemented models which include YOLOV5, UNET and SSD. The satellite images taken over the years are used to generate a new data set and then the produced dataset is augmented using the techniques of rotating, flipping, and patching. The augmented data set is fed into the object detection and image segmentation models for training. The models are then compared on the basis of loss and accuracy to see which model was better suited to carry future work. The concluding results gave accuracy of 32%, 81%, and 24% for the YOLO, UNET and SSD models respectively. Future improvements include the use of high-resolution images and a larger data set to enhance the accuracy of the resulting models.

This paper proposes a data augmentation strategy for improving on-road object detection based on a deep neural network. The method uses a single camera and detects objects based on an optimized deep neural network for a driving environment. The strategy also uses a single-shot multi-box detector (SSD) for object detection, which is a state-of-the-art deep-learning algorithm. The performance is improved by using data augmentation for an advanced driver assist system (ADAS) specific to on-road object recognition. The problem of object detection is first analyzed based on a deep neural network in the ADAS domain, and then representative object detection methods that use deep neural networks are surveyed. A restricted random crop process is suggested for detecting small objects in an image, and then a patch resampling strategy is proposed for solving the long tail property in an on-road dataset. The proposed ADAS domain-specific data augmentation method is adjusted for the original object detection method based on a deep neural network. The object detection results were evaluated using an embedded board on the KITTI benchmark dataset, and the suggested data augmentation method improves the average precision by 30%.

Vegetation cover classification using mixed or low-resolution scalar images is challenging. Fortunately, recently deep learning object detection methods have emerged as a replacement to the conventional machine learning methods for the detection and classification of land use and land cover. This paper presents a deep learning object detection approach for land use and land cover detection using low/mixed resolution satellite images acquired from Google Earth satellite images. Google Earth images are accessible freely using the Google Earth Pro desktop application. Our dataset consists of two (02) classes (vegetation and non-vegetation) with a total of 450 labeled images captured from different parts of Pakistan. We present a comparison of the recent anchor-free object detection model YOLOX with the anchor-based object detection model YOLOR for solving real-time problems. The end-to-end differentiability, efficient GPU utilization, and absence of hand-crafted parameters make anchor-free models a compelling choice in object detection, and yet not been explored on Land cover classification using satellite images. Our experimental study shows that YOLOX delivers an overall accuracy of 83.50% on Vegetation and 86% on Non-Vegetation classes, which outperformed YOLOR by 30% on Vegetation classes and 34% on non-Vegetation classes for our dataset. We also show how an object detection system can be used for Vegetation and Non-Vegetation classification tasks, which can then be used for change monitoring and assisting in developing geographical maps using low/mixed resolution freely available satellite images.

Deep learning has revolutionized the performance of classification and object detection, but meanwhile demands sufficient labeled data for training. Given insufficient data, while many techniques have been developed to help combat overfitting, the challenge remains if one tries to train deep networks, especially in the ill-posed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extremely low data regimes</i> : only a small set of labeled data are available, and nothing – including unlabeled data – else. Such regimes arise from practical situations where not only data labeling but also data collection itself is expensive. We propose a deep adversarial data augmentation (DADA) technique to address the problem, in which we elaborately formulate data augmentation as a problem of training a class-conditional and supervised generative adversarial network (GAN). Specifically, a new discriminator loss is proposed to fit the goal of data augmentation, through which both real and augmented samples are enforced to contribute to and be consistent in finding the decision boundaries. Tailored training techniques are developed accordingly. To quantitatively validate its effectiveness, we first perform extensive simulations to show that DADA substantially outperforms both traditional data augmentation and a few GAN-based options. We then extend experiments to three real-world small labeled classification datasets where existing data augmentation and/or transfer learning strategies are either less effective or infeasible. We also demonstrate that DADA to can be extended to the detection task. We improve the pedestrian synthesis work by substitute for our discriminator and training scheme. Validation experiment shows that DADA can improve the detection mean average precision (mAP) compared with some traditional data augmentation techniques in object detection. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SchafferZhang/DADA</uri> .

Data augmentation (DA) increases the diversity of training data to improve the model generalization ability. Most of the DA methods focus on image classification or object detection. Directly using the existing DA methods on change detection tasks not only falls short of fully exploring the specificity of the change image pairs but also leads to longer training times. In this article, we first propose a mask-guided mixing (MGM) DA for change detection, which mixes the change regions of the current training sample based on prediction results and labels to generate high-quality samples with more positive samples. We then propose a new reinforcement learning (RL)-based Adaptive DA method, AdaAug+, to adaptively select the optimal DA policy for the training samples. An actor selects the best augmentation operation from the operation set according to the image pair. The augmented image pairs make it easier for the change detector to learn the optimal parameters and improve the final detection performance. To reduce the training time, we identify and remove the redundant training samples during the training process by our redundancy searching policy. We have conducted various experiments on four remote sensing change detection datasets with different change detectors. The experimental results demonstrate that AdaAug+ achieves promising performance compared to the state-of-the-art DA methods and requires less training time.

This paper presents an Attention-based Data Augmentation (ADA) approach that extracts keywords from minority class data points using a vector similarity-based mechanism, uses the extracted keywords to extract significant contextual words from minority class documents using an attention mechanism, and uses the significant contextual words to enrich the minority class dataset. By creating new documents based on significant contextual words and adding them to the minority class dataset, we oversample the dataset for the minority class. On the classification job, we compare the original and oversampled versions of the datasets. We also compare ADA over the augmented datasets with two popular state-of-the-art text data augmentation methods. According to the experimental findings, classification algorithms perform better when used to augmented datasets produced by any data augmentation technique than when applied to the datasets’ original versions. Additionally, the classifiers trained over the augmented datasets generated by ADA are more effective than those generated by state-of-the-art data augmentation techniques.

Safe self-driving vehicles require precise 3D Object Identification. LiDAR sensors are key in accomplishing such a task, as LiDARs produce high-definition point clouds. Such point clouds are then processed by 3D Object Detection models to finally detect objects.Most Object Detection models require massive amounts of data to be trained. Gathering and processing this data is an expensive and time-consuming task, which is why the information taken from each sample must be fully harnessed. Such can be done through Data Augmentation. Data Augmentation contributes significantly for improving performance, being at least as relevant as the advances in the Object Detection models themselves.A few studies have been reported regarding the effectiveness of Data Augmentation. However, the role played by original and augmented samples has been neglected. This work reports the first-ever detailed quantification of the impact that the inclusion of original and augmented samples in a dataset has in 3D Object Detection in the context of autonomous driving. The obtained results show that although a good augmentation strategy is crucial to the model’s performance, it is only as good as the quality of the original samples allows it to be.

Medical imaging datasets, particularly for chest X-rays, often suffer from limited size, class imbalance, and privacy constraints, leading to suboptimal performance in deep learning-based pneumonia detection models. This paper presents a Deep Convolutional Generative Adversarial Network (DCGAN)-based approach to generate synthetic chest X-ray images of the minority class (normal lungs) to augment the training dataset. The publicly available Kaggle Chest X-ray Pneumonia dataset was used, where the minority class was augmented with 2000 synthetic images generated by a trained DCGAN. Two identical Convolutional Neural Network (CNN) classifiers were trained: Model A on real images only and Model B on the augmented dataset (real + synthetic). Experimental results show an improvement in test accuracy from 71.31% (real only) to 74.68% (real + synthetic), demonstrating the effectiveness of GAN-based augmentation in addressing class imbalance and enhancing model generalization. Visual inspection of generated images and GAN training loss curves confirm realistic synthesis. This approach highlights GANs as a valuable tool for overcoming data scarcity in medical image analysis without compromising patient privacy. Keywords: Generative Adversarial Networks, Data Augmentation, Chest X-ray, Pneumonia Detection, Deep Convolutional GAN, Class Imbalance.

This paper introduces VERA (Vision Expert Real Analysis), a privacy-supporting cyber-physical framework designed for real-time data collection and visual analysis in healthcare environments. VERA limits exposure to identifiable RGB content by ensuring that annotators interact only with non-identifiable edge-based representations, while original images remain encrypted at rest using AES-CFB, with integrity verification performed before in-memory decryption. The system integrates edge-based obfuscation, secure annotation, in-memory decryption, and dynamic data augmentation to train YOLO-based person detection models without compromising patient privacy. Experimental results on a curated COCO subset show that VERA enables effective person detection, improving mean Average Precision (mAP) from an intentionally minimal baseline of 0.61 percent to 99.94 percent after full training and augmentation. This baseline is used solely to illustrate the contribution of the secure data preparation pipeline and is not intended to represent a fully optimized YOLO configuration. The results demonstrate that privacy-supportive workflows can maintain strong model performance while aligning with data protection practices common in regulated environments. Although this work focuses on person detection as a foundational stage, the VERA architecture is designed to support future extensions toward privacy-preserving Human Activity Recognition (HAR) tasks in clinical and assisted-living settings.

ObjectiveAutomatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.MethodsThis research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.ResultsDue to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.ConclusionA deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Air-to-ground object detection has important application value in many fields such as military reconnaissance, air strike, personnel rescue, and natural exploration. Object detection algorithm based on deep learning solves the problems of poor generality and low robustness of traditional manual detection algorithms. It has great advantages in air-to-ground detection tasks, but for small unmanned platforms, currently deep learning object detection algorithms have high hardware requirements and are difficult to deploy. In this paper, a deep learning object detection simulation system is built based on the TX2 embedded development board with the features of low power consumption, light weight and fast calculation speed. This object detection simulation experiment is performed for two mainstream deep learning object detection algorithms. The experimental results showed that the regression-based SSD algorithm combined with MobileNets feature extraction network could perform real-time in the embedded system, which laid the foundation for the actual construction of the next unmanned aerial platform.

Dental caries, a common oral disease, poses serious risks if untreated, necessitating effective preventive measures like pit and fissure sealing. However, the reliance on experienced dentists for pit and fissures or caries detection limits accessibility, potentially leading to missed treatment opportunities, especially among children. To bridge this gap, we leverage deep learning in object detection to develop a method for autonomously identifying caries and determining pit and fissure sealing requirements using smartphone oral photos. We test several detection models and adopt a tiling strategy to reduce information loss during image pre‐processing. Our implementation achieves 72.3 mAP.5 with the YOLOXs model and tiling strategy. We enhance accessibility by deploying the pre‐trained network as a WeChat applet on mobile devices, enabling in‐home detection by parents or guardians. In addition, our data set of children's first permanent molars will also aid in the broader study of pediatric oral disease.

Data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.