International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
www.irjet.net
A Review of Object Detection Using Deep Learning and LSTM for Enhanced Accuracy in Sequential Data Analysis Divya Rai1, Dipti Ranjan Tiwari2 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 - Object detection is a critical task in computer
vision, enabling machines to identify and localize objects within images or videos. With the advent of deep learning, the performance of object detection models has significantly improved, particularly through the use of Convolutional Neural Networks (CNNs). However, the challenge remains in handling sequential data, where the temporal context of objects in videos or time-series data can improve detection accuracy. This review paper explores the integration of Long Short-Term Memory (LSTM) networks with deep learning techniques for object detection, focusing on enhancing the accuracy and efficiency of models when dealing with sequential data. The paper reviews recent advancements in hybrid models combining CNNs and LSTMs, highlighting their ability to capture spatial and temporal dependencies in data, which are crucial for dynamic environments. Furthermore, it examines the performance of various architectures in realworld applications, such as autonomous driving, surveillance, and robotics. The review also discusses challenges, such as model interpretability, data quality, and computational complexity, while suggesting potential future research directions to further enhance the effectiveness of object detection using deep learning and LSTMs.
boundaries of object detection, making it more accurate and robust for complex real-world scenarios.
2.IMPORTANCE OF ACCURATE OBJECT DETECTION IN REAL-TIME APPLICATIONS Accurate object detection in real-time applications is critical because it directly impacts the effectiveness and safety of systems that rely on immediate, informed decision-making. In fields like autonomous driving, precise and rapid identification of pedestrians, vehicles, and road signs is essential for navigating complex environments and avoiding accidents. In security and surveillance, real-time object detection enables prompt identification of suspicious activities or threats, improving response times. In robotics and industrial automation, accurate detection ensures efficient task execution, from object manipulation to quality control. Moreover, in healthcare, timely detection of abnormalities in medical imaging can significantly improve diagnosis and treatment outcomes. Therefore, the ability to detect objects quickly and accurately in dynamic, real-world scenarios is essential for ensuring safety, reliability, and operational efficiency across a wide range of critical applications.
Key Words: Object Detection, Deep Learning, Long ShortTerm Memory (LSTM), Sequential Data Analysis, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Feature Extraction, Temporal Analysis.
1.OBJECT DETECTION IN COMPUTER VISION. Object detection in computer vision is a crucial task that involves identifying and localizing objects within images or videos by predicting their class labels and drawing bounding boxes around them. Initially relying on traditional methods like feature extraction and sliding window techniques, the field has been revolutionized by deep learning approaches, particularly Convolutional Neural Networks (CNNs). Models like R-CNN, YOLO, SSD, and Faster R-CNN have significantly improved detection accuracy and speed, enabling real-time applications in areas such as autonomous driving, surveillance, healthcare, and robotics. Despite advances, challenges remain, including handling occlusions, scale variations, small object detection, and class imbalance. Ongoing research in transformer-based models, multi-modal detection, and transfer learning continues to push the © 2024, IRJET
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Impact Factor value: 8.315
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Figure-1: IMPORTANCE of Accurate Object Detection in Real-Time Applications. ISO 9001:2008 Certified Journal
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