Enhancing Wildlife Tourism Management Using Deep Learning and Particle Swarm Optimization (PSO) for Animal Detection in Wildlife Sanctuaries

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Year : 2024 | Volume : 14 | Issue : 03 | Page : 41 50
    By

    Neeraj Kishor Pawar,

  • Dev Harish Jain,

  • Bindi Prafulbhai Gondalia,

  • Palak Rajesh Singh,

  • Aditya Suresh Kasar,

  1. Student,, Department of Electronics & Communication Engineering, SVKM’s NMIMS School of Technology and Management, Navi Mumbai, Maharashtra, India
  2. Student,, Department of Electronics & Communication Engineering, SVKM’s NMIMS School of Technology and Management, Navi Mumbai, Maharashtra, India
  3. Student,, Department of Electronics & Communication Engineering, SVKM’s NMIMS School of Technology and Management, Navi Mumbai, Maharashtra, India
  4. Student,, Department of Electronics & Communication Engineering, SVKM’s NMIMS School of Technology and Management, Navi Mumbai, Maharashtra, India
  5. Assistant Professor, , Department of Electronics & Communication Engineering, SVKM’s NMIMS School of Technology and Management, Navi Mumbai,, Maharashtra, India

Abstract

Wildlife tourism is one of the most thriving sectors, faced with huge challenges in terms of safeguarding protected areas. As demand for wildlife experiences accelerates, it becomes necessary to find efficient measures that are friendly to conservation. The use of these advanced techniques in this field such as YOLO and PSO algorithm presents a new dimension on managing wildlife tourism. To harness the abilities of these techniques, this research centers on deep learning approaches as well as PSO algorithms. To train our animal detection model, a custom animal dataset was created specifically for this purpose and was annotated using the Computer Vision Annotation Tool (CVAT). The dataset encompasses various sources of animal images and includes examples from species regularly encountered by tourists when they go on safari, thus capturing some diversity. It is after annotation that we can use it as the basis for training our precise object detecting algorithm which is YOLOv8s. Hyperparameter tuning was done for optimizing the model using PSO algorithm by obtaining the best fit parameters. This optimization process involves enhancing the detection precision through improving the model’s ability to recognize different animals within various environmental conditions. The model is assessed over curated datasets to make sure that it does well in the real-world scenarios with Map accuracy of 81%. Furthermore, the model is equipped with animal tracking on video data. This aspect enables the possibility of monitoring and protective measures. The purpose of this research is to solve the major problems in the management of wildlife tourism. The sophistication of the developed model with features like real-time tracking enhances the scope for proactive management of wildlife tourism activities, customer satisfaction and conservation efforts.

Keywords: Object detection, Prediction, Training, gradient class activation map, transfer learning, validation.

[This article belongs to Current Trends in Signal Processing ]

How to cite this article:
Neeraj Kishor Pawar, Dev Harish Jain, Bindi Prafulbhai Gondalia, Palak Rajesh Singh, Aditya Suresh Kasar. Enhancing Wildlife Tourism Management Using Deep Learning and Particle Swarm Optimization (PSO) for Animal Detection in Wildlife Sanctuaries. Current Trends in Signal Processing. 2024; 14(03):41-50.
How to cite this URL:
Neeraj Kishor Pawar, Dev Harish Jain, Bindi Prafulbhai Gondalia, Palak Rajesh Singh, Aditya Suresh Kasar. Enhancing Wildlife Tourism Management Using Deep Learning and Particle Swarm Optimization (PSO) for Animal Detection in Wildlife Sanctuaries. Current Trends in Signal Processing. 2024; 14(03):41-50. Available from: https://journals.stmjournals.com/ctsp/article=2024/view=183910


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Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 02/08/2024
Accepted 21/08/2024
Published 18/11/2024
307

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