An Optimized Model for the Segmentation of the Ancient Temple Vimanas using FCN Network

Authors

  • Narendra Kumar S. J.N.N College of Engineering, Shivamoga-577204, Visvesvaraya Technological University, Belagavi-590018, INDIA.
  • Shrinivasa Naik C. L. U.B.D.T College of Engineering, Davanagere-577004, Visvesvaraya Technological University, Belagavi-590018, INDIA.
  • Gurudeva Shastri Hiremath St. Joseph Engineering College, Mangaluru-575028 , Visvesvaraya Technological University, Belagavi-590018, INDIA.

Keywords:

Archaeology, segmentation, vimana, Fully Convolutional Network (FCN), hyper parameters, recall, precision, Dice correlation coefficient

Abstract

An extensive collection of artifacts, antiquities that are historically and archaeologically significant monuments is housed in the Indian state of Karnataka. Tradition and culture are intricately linked. Karnataka boasts a multitude of Neolithic and Megalithic structures, which have withstood the test of time for millennia. These architectural marvels are remnants of esteemed ruling dynasties. They possess unique wonders characterized by their distinctive style, inherent sculptural and architectural qualities, technical prowess, vastness, and grandeur. Nevertheless, the current generation is ill-prepared to extract archaeological knowledge pertaining to empires or reigning dynasties of these ancient Karnataka temples under the instruction of archaeologists. Therefore, it is necessary to adopt a novel method to effectively deliver this vital information to the contemporary age through a suitable platform. Archaeologists have numerous intricate challenges due to the absence of reliable digital techniques for automatically segmenting Vimana. Automated segmentation of Vimana poses challenges due to the variability in image acquisition, intricate architectural designs, noise, time difficulties, and photographic artifacts. As per our knowledge techniques for segmentation have not been proposed in the literature for vimana segmentation. Our work introduces a optimized fully convolutional network (FCN) model designed specifically for the automated segmentation of Vimana. The suggested approach mitigates the variability of image noise and trains Fully Convolutional Network (FCN) models using images from our custom dataset. Additionally, it has been demonstrated that employing appropriate data augmentation and model hyper-parameterization effectively mitigates over-fitting in the context of vimana area segmentation. The proposed methodology is evaluated using the test dataset, attaining a rate of recall of 0.9302 and a precision rate of 0.8977. The recommended method outperforms four other methods with lower depths in the segmentation problem, earning a Dice correlation coefficient of 0.8894 & with very min loss of around 0.1106. Finally a comparison of same methods with & without edge-smoothing is carried out. An improvement of 12%, 28% is achieved in DICE & PRECISION by an optimized FCN(U-Net) for the segmentation of vimana.

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Published

24.03.2024

How to Cite

Kumar S., N. ., C. L., S. N. ., & Shastri Hiremath, G. . (2024). An Optimized Model for the Segmentation of the Ancient Temple Vimanas using FCN Network . International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 568–576. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5100

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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