Wild Animals Intrusion Detection for Safe Commuting in Forest Corridors using AI Techniques

Raj, J. Joshua Daniel; Sangeetha, C.; Ghorai, S.; Das, Subhajit; Manish,; Ahmed, Shariq

doi:10.1109/iciptm57143.2023.10117831

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2024

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Cited by 6 publications

References 11 publications

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A Modified AI Model for Automatic and Precision Monitoring System of Wildlife in Forest Areas

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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A Modified AI Model for Automatic and Precision Monitoring System of Wildlife in Forest Areas

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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An IoT-based animal detection system using an interdisciplinary approach

Mamidi,

Valiveti,

Vutukuri

et al. 2024

E3S Web of Conf.

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Nowadays, educational institutions particularly colleges, engaged with students and staff, frequently confront various security challenges in their day-to-day activities. One prominent concern involves the threat of animal bites on the campus. In response to this issue, campus management has traditionally resorted to human patrols and physical barriers to deter animals. To address this multifaceted security challenge, the proposed method “An IoT-based Animal Detection System Using Interdisciplinary Approaches” introduces an innovative solution that leverages the power of IoT technology to enhance campus safety and security significantly. The system deploys a surveillance robot equipped with ultrasonic sensors and ESP32 cameras, employingthe machine learning technique R-CNN for Animal Detection. This proposed method uses an interdisciplinary approach to develop an animal detection system capable of identifying and classifying various species. This proposed method aims to revolutionize campus security by seamlessly integrating advanced technology, mitigating risks proactively, streamlining processes through automation, and presenting a cost-effective alternative to traditional security approaches. Beyond the traditional methods, the proposed system achieves an impressive accuracy rate of animal detection approximately 97.6% enabling real-time alerts through push notifications to security personnel upon detection.

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Enhancing Animal Intrusion Detection with Raspberry Pi and Fast R-CNN: A Practical Approach for Wildlife Management

Kanthimathi,

Daniel,

Krishnan

et al. 2024

2024 8th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Wild Animals Intrusion Detection for Safe Commuting in Forest Corridors using AI Techniques

Cited by 6 publications

References 11 publications

A Modified AI Model for Automatic and Precision Monitoring System of Wildlife in Forest Areas

A Modified AI Model for Automatic and Precision Monitoring System of Wildlife in Forest Areas

An IoT-based animal detection system using an interdisciplinary approach

Enhancing Animal Intrusion Detection with Raspberry Pi and Fast R-CNN: A Practical Approach for Wildlife Management

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