International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Object Detection Using Deep Learning and LSTM for Enhanced Accuracy in Sequential Data Analysis Divya Rai1, Arifa Khan2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The detection of objects in sequential data
enhancement of gesture recognition as well as deployment of augmented reality attributes and digital structure interactive capabilities.
formats like video frames or time-series images faces specific obstacles because frames naturally demonstrate interdependent relationships throughout time sequences. The ability to extract spatial features with Convolutional Neural Networks (CNNs) reaches exceptional results but such deep learning approaches face difficulties in processing temporal information essential to detect objects reliably in sequential data. We introduce a hybrid system that connects spatial feature identification through CNNs with Temporal modeling capabilities from Long Short-Term Memory (LSTM) networks. The ensemble joins CNN spatial detection capabilities with LSTM temporal modeling to fully extract scene information across consecutive video frames. Our model examination on standard benchmark data sets produces noteworthy results with enhanced object detection precision than individual CNN approaches for spatial pattern processing and time-series imaging analysis. The experimental findings show that the CNN-LSTM architecture delivers superior outcomes for space-based detection alongside temporal object identification. This solution enables object detection through a flexible system which benefits autonomous vehicles alongside surveillance and medical imaging operations that need precise sequential dataset analysis. Research in hybrid deep learning models has expanded as this work introduces a successful solution to enhance object detection within sequential database analysis.
Figure-1: Overview of Deep Learning. Although traditional object detection techniques have achieved notable improvements, they have difficulty with processing sequential data. Existing detection algorithms are usually about spatial data, rather than temporal video data. The results of these traditional methods are not sufficiently accurate in dynamic environments due to failure to consider patterns in sequential terms and contextual information. However, traditional models run into particular difficulties in these cases: fast moving objects, or objects that become hidden from view.
Key Words: Object Detection, Deep Learning, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Sequential Data Analysis.
1.INTRODUCTION 1.1.Background and Motivation Object detection is an essential operation in the various applications of computer vision, from surveillance systems, autonomous vehicles and human computer interaction. Accurate objects detection during a surveillance operation is heavy reliant on efficient monitoring plus threat identification. With that, it has become necessary for detecting pedestrians along with vehicles and obstacles so that autonomous vehicles can navigate safely. Human computer interaction object detection allows the
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Figure-2: Application of Deep Learning.
1.2.Research Problem Object detection today relies mainly on spatial analysis through Convolutional Neural Networks to extract visual features. Static image detection methods succeed through
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