A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

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Comparative analysis of deep learning image detection algorithms

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management. 

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Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

With the unprecedented growth of wireless body area network (WBAN) users and computation-intensive 5G-based healthcare applications, mobile edge computing (MEC)-enabled healthcare systems that enable computation offloading to edge servers in proximity, are gaining much interest. However, due to the ever-increasing requirement of WBAN users’ quality of experience (QoE), the computational load on the MEC server increases, resulting in high energy costs and heavy carbon emissions. Therefore, in this paper, we focus on joint cost and energy-efficient task offloading in the MEC-enabled healthcare system by designing incentives for WBAN users to curtail their amount of task offloading. In particular, we model the interaction among the MEC server and WBAN users using the Stackelberg game and derive the optimal task offloading decision for WBAN users and corresponding reimbursement amount. As the number of WBAN users is large, we propose an alternating direction method of multipliers (ADMM)-based algorithm to achieve the optimal solution in a distributed manner. Further, simulation results show that the proposed algorithm maximizes the payoffs of both the MEC server and the WBAN users, while also reducing the MEC server energy cost by 52.38% compared to benchmark schemes.

Enabling Green Mobile-Edge Computing for 5G-Based Healthcare Applications

In the contemporary era, the global explosion of traffic has created many eye-catching concerns for policymakers. This not only enhances pollution but also leads to several road accident fatalities which may be greatly reduced by proper monitoring and surveillance. Further, with the advent of UAV technology and due to the incompatibility of traditional techniques, surveillance has become one of UAVs prominent application domains. However, it requires algorithmic analysis of aerial images which becomes extremely challenging due to multi-scale rotating objects with large aspect ratios, extremely imbalanced categories, cluttered background, and birds-eye view. Therefore, this article presents the novel aerial image traffic monitoring and surveillance algorithms based on the most advanced and popular DL object detection models (Faster-RCNN, SSD, YOLOv3, and YOLOv4) using the AU-AIR dataset. This dataset is exceedingly imbalanced and to resolve this issue, another 500 images have been grabbed by web-mining techniques. The novel contribution of this work is two-fold. First, this article scientifically distinguishes the inappropriateness of ground-view images for aerial object detection. Second, a regress comparison of these algorithms has been made to investigate their effectiveness. Extensive experimental analysis endorses the efficiency of YOLOv4 as it outperforms the other developed models by a minimum mAP margin of 88%. Also, more than 6 times high detection speed and greater adaptability with stronger detection robustness ensure its real-time practical implementation.

Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms. 

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Deep learning techniques to classify agricultural crops through UAV imagery: a review

Deep learning-based object detection in low-altitude UAV datasets: A survey

The low-altitude aerial objects are hard to detect by existing deep learning-based object detectors because of the scale variance, small size, and occlusion-related problems. Deep learning-based detectors do not consider contextual information about the scale information of small-sized objects in low-altitude aerial images. This paper proposes a new system using the concept of receptive fields and fusion of feature maps to improve the efficiency of deep object detectors in low-altitude aerial images. A Dilated ResNet Module (DRM) is proposed, motivated from the trident networks, which works on dilated convolutions to study the contextual data for specifically small-sized objects. Applicability of this component builds the model strong towards scale variations in low-altitude aerial objects. Then, Feature Fusion Module (FFM) is created to offer semantic intelligence for better detection of low-altitude aerial objects. We have chosen vastly deployed faster RCNN as the base detector for the proposal of our technique. The dilated convolution-based RCNN using feature fusion (DCRFF) system is implemented on a benchmark low-altitude UAV based-object detection dataset, VisDrone, which contains multiple object categories of pedestrians, vehicles in crowded scenes. The experiments exhibit the enactment of the given detector on chosen low-altitude aerial object dataset. The proposed system of DCRFF achieves 35.04% mAP on the challenging VisDrone dataset, indicating an average improvement of 2% when compared. 

https://uws.pure.elsevier.com/files/30425949/2022_03_28_Mittal_et_al_Dilated_accepted.pdf

Dilated convolution based RCNN using feature fusion for Low-Altitude aerial objects

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures. 

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Payal Mittal

Papers

Deep learning-based object detection in low-altitude UAV datasets: A survey

Dilated convolution based RCNN using feature fusion for Low-Altitude aerial objects

On the performance evaluation of object classification models in low altitude aerial data

Power‐saving policies for annual energy cost savings in green computing

A modified U-net-based architecture for segmentation of satellite images on a novel dataset