Object Detection and Recognition using Amazon Rekognition with Boto3

Sharma, Vivek

doi:10.1109/icoei53556.2022.9776884

Cited by 11 publications

(3 citation statements)

References 9 publications

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“…Finally, Sharma (26) and Mohanta and Sethi ( 27) provide research on multiclass object detection and classification models using Amazon Rekognition. The former focuses on multiobject detection using custom labels, whereas the latter bases their study on using Amazon Rekognition for image pattern detection.…”

Section: Related Workmentioning

confidence: 99%

“…presents the parameters used to train the extended dataset with YOLOv5.YOLOv7 model training is similar; however, there are two key differences, that is, the specifications using YOLOv7 training weights with the -weights argument and the key argument of -device 0 to specify which CUDA(30) device to utilize for training to utilize YOLOv7 greater CUDA optimization [(5),(21), and(26)]. …”

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Object detection and ship classification using YOLO and Amazon Rekognition

Perez,

Bagui

2023

BIJIAM

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The need for reliable automated object detection and classification in the maritime sphere has increased significantly over the last decade. The increased use of unmanned marine vehicles necessitates the development and use of an autonomous object detection and classification systems that utilize onboard cameras and operate reliably, efficiently, and accurately without the need for human intervention. As the autonomous vehicle realm moves further away from active human supervision, the requirements for a detection and classification suite call for a higher level of accuracy in the classification models. This paper presents a comparative study using different classification models on a large maritime vessel image dataset. For this study, additional images were annotated using Roboflow, focusing on the types of subclasses that had the lowest detection performance, a previous work by Brown et al. (1). The present study uses a dataset of over 5,000 images. Using the enhanced set of image annotations, models were created in the cloud using Google Colab using YOLOv5, YOLOv7, and YOLOv8 as well as in AmazonWeb Services using Amazon Rekognition. The models were tuned and run for five runs of 150 epochs each to collect efficiency and performance metrics. Comparison of these metrics from the YOLO models yielded interesting improvements in classification accuracies for the subclasses that previously had lower scores, but at the expense of the overall model accuracy. Furthermore, training time efficiency was improved drastically using the newer YOLO APIs. In contrast, using Amazon Rekognition yielded superior accuracy results across the board, but at the expense of the lowest training efficiency.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Object detection and ship classification using YOLO and Amazon Rekognition

Perez,

Bagui

2023

BIJIAM

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“…In essence, this study aims to answer the question: how does the integration of Delaunay triangulation with multipleunmanned aerial vehicle systems enhance object detection accuracy in comparison to traditional single UAV approaches? The image processing methodology in this research leverages Amazon Rekognition, a robust and scalable solution for object detection in aerial surveillance [13]. Amazon Rekognition offers a pre-trained deep learning model capable of identifying and locating objects within images, making it suitable for real-time analysis of UAV-captured data.…”

Section: Introductionmentioning

confidence: 99%

Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems

Alotaibi,

Chatwin,

Birch

2024

ASI

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In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.

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