Automated Image Captioning Using Deep Learning

Authors

  • Chandra B., Avinash P., Sai Prasath P., Jennet Shinny D., Keshav Adhitya M.

Keywords:

Object Detection, Deep Learning, Computer Vision, YOLOv3, Convolutional Neural Networks

Abstract

Object detection, pivotal in computer vision, spans diverse applications like autonomous driving, medical imaging, etc. Deep learning, notably, enhances detection by hierarchically representing data. Two prevalent approaches are region proposal-based (e.g., R-CNN, Fast R-CNN) and unified pipeline-based (e.g., YOLOv2). The latter, exemplified by YOLOv2, emphasizes speed and simplicity. Innovations like batch normalization and anchor boxes refine accuracy. Variants like real-time YOLO adapt for specific platforms (e.g., Non-GPU computers), while methods like SSD and DSSD optimize speed and accuracy trade-offs. Recent advancements include YOLOv3's binary cross-entropy loss for improved small object detection

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Published

27.03.2024

How to Cite

Avinash P., Sai Prasath P., Jennet Shinny D., Keshav Adhitya M., . C. B. (2024). Automated Image Captioning Using Deep Learning . International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1418–1421. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5534

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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