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A novel framework for human object classification in flooded environments using YOLOv12n and FastGRNN-MBT.

Pallavi Nehete1,2, Madhuri Dharrao3, Anupkumar M Bongale4

  • 1Department of Computer Science and Engineering Symbiosis International (Deemed University) , Symbiosis Institute of Technology , Maharashtra, Pune Campus, Lavale, Pune, India.

Scientific Reports
|April 28, 2026
PubMed
Summary

Detecting humans in floods is crucial. A new Fast Gated Recurrent Neural Network-based Mean Binary cross entropy Taylor concept (FastGRNN-MBT) method achieves high accuracy in classifying human objects during flood disasters.

Keywords:
Dual Tree- Local Phase Quantization with entropyFast Gated Recurrent Neural NetworkMean Binary cross entropyObject detectionTaylor seriesYou Only Look Once version 12 nano

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Floods are devastating natural disasters causing significant destruction.
  • Accurate human object detection in flooded environments is critical for disaster response.
  • Existing methods often lack high classification accuracy and misclassify objects.

Purpose of the Study:

  • To present a novel Fast Gated Recurrent Neural Network-based Mean Binary cross entropy Taylor concept (FastGRNN-MBT) for human object classification in flooded environments.
  • To improve the accuracy and reliability of human detection during flood disasters.

Main Methods:

  • Image pre-processing using adaptive median filter and Retinex algorithm for enhancement.
  • Object detection utilizing You Only Look Once version 12 nano (YOLOv12n), fine-tuned with the CAO algorithm.
  • Feature extraction and final human object classification using the proposed FastGRNN-MBT model.

Main Results:

  • The FastGRNN-MBT model achieved high performance metrics: 96.979% recall, 97.763% precision, 96.226% accuracy, and 96.988% F1-score.
  • Demonstrated significantly improved classification accuracy compared to existing methods.
  • Achieved efficient execution and inference times (147.236s and 28.178s, respectively).

Conclusions:

  • The FastGRNN-MBT model is highly effective and suitable for classifying human objects in flooded environments.
  • This method offers a significant advancement in disaster response technology for human detection.
  • The proposed approach outperforms current state-of-the-art classification methods.