SSE: A Smart Framework for Live Video Streaming based Alerting System

Kumar, Aman; Hashmi, Hina; Khan, Shueb Ali; Naqvi, S. Kazim

doi:10.1109/smart52563.2021.9675306

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…ML is used to build smart systems by implementing various learning algorithms. With the help of these ML algorithms, the computers can perform various tasks such as object detection, recognition, localization, and classi ication [3] in images as well as the applications such as fraud detection in live videos as well [4]. These automated systems are developed by ML algorithms to learn from training images and the model is smart enough to keep itself updated while producing outputs without any human interaction.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

Hashmi,

Kumar Dwivedi,

Kumar

2024

JAMRIS

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In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

Hashmi,

Kumar Dwivedi,

Kumar

2024

JAMRIS

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YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing Object Detection

Hashmi,

Dwivedi,

Kumar

2023

2023 12th International Conference on System Modeling &Amp; Advancement in Research Trends (SMART)

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Smart edge segmentation and localization method for building detection in satellite imagery

Hashmi,

Dwivedi,

Kumar

et al. 2024

IFS

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The rapid advancements in satellite imaging technology have brought about an unprecedented influx of high-resolution satellite imagery. One of the critical tasks in this domain is the automated detection of buildings within satellite imagery. Building detection holds substantial significance for urban planning, disaster management, environmental monitoring, and various other applications. The challenges in this field are manifold, including variations in building sizes, shapes, orientations, and surrounding environments. Furthermore, satellite imagery often contains occlusions, shadows, and other artifacts that can hinder accurate building detection. The proposed method introduces a novel approach to improve the boundary detection of detected buildings in high-resolution remote sensed images having shadows and irregular shapes. It aims to enhance the accuracy of building detection and classification. The proposed algorithm is compared with Customized Faster R-CNNs and Single-Shot Multibox Detectors to show the significance of the results. We have used different datasets for training and evaluating the algorithm. Experimental results show that SESLM for Building Detection in Satellite Imagery can detect 98.5% of false positives at a rate of 8.4%. In summary, SESLM showcases high accuracy and improved robustness in detecting buildings, particularly in the presence of shadows.

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SSE: A Smart Framework for Live Video Streaming based Alerting System

Cited by 3 publications

References 15 publications

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing Object Detection

Smart edge segmentation and localization method for building detection in satellite imagery

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