Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals using Modified Convolutional Neural Network Technique compared with Support Vector Machine

Pooja, Ch.; Jaisharma, K.

doi:10.1109/icbats54253.2022.9759020

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…The real photos were pre-processed and then tweaked for background brightness uniformity and vignetting removal. Convolutional neural networks excel in object identification and classification, but they also exceed any of the aforementioned methods as well as any ML method, as shown in the study (Pooja and Jaisharma, 2022). Here, the authors used sophisticated security cameras to achieve the highest level of object detection accuracy.…”

Section: ) Reinforcement Learning Based Techniquesmentioning

confidence: 99%

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Jani

2023

IJIUS

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PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

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Section: ) Reinforcement Learning Based Techniquesmentioning

confidence: 99%

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Jani

2023

IJIUS

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“…After a pre-processing step, the actual images were optimized for vignetting reduction and background brightness uniformity. In object recognition and classification, convolutional neural networks outperform not only any of the techniques mentioned above but also any ML technique, such as demonstrated in the work [ 99 ]. Here, the authors obtained the best accuracy of the detected image objects by using intelligent surveillance cameras.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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Novel Framework for the Improvement of Object Detection Accuracy of Smart Surveillance Camera Visuals using Modified Convolutional Neural Network Technique compared with Support Vector Machine

Cited by 2 publications

References 23 publications

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

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