dc.contributor.author | TUSHAR | |
dc.contributor.author | Kumar, Kundan | |
dc.date.accessioned | 2024-09-20T07:58:07Z | |
dc.date.available | 2024-09-20T07:58:07Z | |
dc.date.issued | 2022-05 | |
dc.identifier.uri | http://10.10.11.6/handle/1/18244 | |
dc.description | SCHOOL OF COMPUTING SCIENCE AND ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
GALGOTIAS UNIVERSITY, GREATER NOIDA, INDIA | en_US |
dc.description.abstract | The most often adopted methodologies for contemporary machine learning
techniques to execute a variety of responsibilities on embedded devices are mobile
networks and multimodal neural networks. In this research, we propose a method
for identifying an item that takes into account the learning - based pre-trained
system MobileNetV3 for an SSD (Single Shot Multibox Detector). This set of rules
is utilized for constant identification, as well as for camera broadcasts to find an
explanation camera which recognizes the thing for a clip transfer. Consequently, I
utilize an item identification module which could find what's withinside the video
transfer. To place into impact the module, we integrate the MobileNetV3 and the
SSD system for a quick and green profound getting to know-primarily based totally
approach of item detection. The primary goal of our study is to investigate the
effectiveness of an object identification technique SSD and the significance of
MobileNetV3. The testing findings demonstrate that the Average Closeness of the
set of rules to discover one of a kind training as car, person and cat is 97.45%,
95.67% and 85.59%, respectively. It enhances the accuracy of content detection at
the processing speed necessary for practical identification and the needs of regular
progress indoors and outdoors. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Galgotias University | en_US |
dc.subject | MobileNetV3, | en_US |
dc.subject | Computer vision, | en_US |
dc.subject | SSD(Single Shot Multibox Detector), | en_US |
dc.subject | OpenCV, | en_US |
dc.subject | Deep Learning Neural Network. | en_US |
dc.title | OBJECT DETECTION USING OPENCV AND DEEP LEARNING | en_US |
dc.type | Technical Report | en_US |