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Background <p>In rural and forest-bordering areas, human-wildlife conflict has escalated, resulting in substantial crop damage, property loss, and human mortality. Conventional mitigating solutions such as fencing, manual surveillance, and local alarms, have limited coverage, time taking and high operating costs. Automated real-time animal detection systems have become a practical for early warning and conflict avoidance due to recent upgrades in edge computing and deep learning particularly in distant locations without dependable internet access.</p> Methods <p>In this study, embedded hardware and deep learning-based object detection are used to create a real-time wild animal detection and warning system. Roboflow was used to develop and enhance a dataset of 1,554 real-world photographs from seven classes (tiger, bear, elephant, lion, cheetah, leopard, and human) to 5,917 images. Using GPU acceleration, three object detection models YOLOv8(n)(n), SSD, and RetinaNet were trained on Google Colab and assessed using precision, recall, F1-score, and mean Average Precision (mAP). A GSM 900&#xa0;A module is used to send SMS notification with no requirement of internet connectivity, and the optimized model was installed on a Raspberry Pi 5 with a Pi Camera for real-time inference.</p> Results <p>According to experimental data, YOLOv8(n) performs better than SSD and RetinaNet, with an mAP@50 84.4% and recall of 81% as opposed to SSD’s 62.3% precision and 58% recall and RetinaNet’s around 77% precision and 80% recall. Additionally, YOLOv8(n) showed improved robustness and quicker inference time across a range of background and lighting circumstances. While SSD and RetinaNet demonstrated weaker or scattered attention in complex situations, explainable AI analysis utilizing Grad-CAM and LIME verified greater and more concentrated activation on pertinent animal traits for YOLOv8(n).</p> Conclusion <p>For real-time wildlife monitoring and early warning, the suggested system offers a precise, affordable, and scalable option. Through the integration of YOLOv8(n), Raspberry Pi 5 edge computing, and GSM-based connectivity, the system efficiently functions in distant settings and dramatically shortens reaction times during wildlife infiltration incidents. The findings demonstrate that YOLOv8(n) is the best model for integrated animal detection, enhancing wildlife conservation, lowering agricultural loss, and improving human safety.</p>

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Interpreting deep learning models for explainable animal detection using Grad-Cam with RetinaNet, SSD and YOLOv8

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Minal Patil,
  • Abhishek Madankar,
  • Roshan Umate

摘要

Background

In rural and forest-bordering areas, human-wildlife conflict has escalated, resulting in substantial crop damage, property loss, and human mortality. Conventional mitigating solutions such as fencing, manual surveillance, and local alarms, have limited coverage, time taking and high operating costs. Automated real-time animal detection systems have become a practical for early warning and conflict avoidance due to recent upgrades in edge computing and deep learning particularly in distant locations without dependable internet access.

Methods

In this study, embedded hardware and deep learning-based object detection are used to create a real-time wild animal detection and warning system. Roboflow was used to develop and enhance a dataset of 1,554 real-world photographs from seven classes (tiger, bear, elephant, lion, cheetah, leopard, and human) to 5,917 images. Using GPU acceleration, three object detection models YOLOv8(n)(n), SSD, and RetinaNet were trained on Google Colab and assessed using precision, recall, F1-score, and mean Average Precision (mAP). A GSM 900 A module is used to send SMS notification with no requirement of internet connectivity, and the optimized model was installed on a Raspberry Pi 5 with a Pi Camera for real-time inference.

Results

According to experimental data, YOLOv8(n) performs better than SSD and RetinaNet, with an mAP@50 84.4% and recall of 81% as opposed to SSD’s 62.3% precision and 58% recall and RetinaNet’s around 77% precision and 80% recall. Additionally, YOLOv8(n) showed improved robustness and quicker inference time across a range of background and lighting circumstances. While SSD and RetinaNet demonstrated weaker or scattered attention in complex situations, explainable AI analysis utilizing Grad-CAM and LIME verified greater and more concentrated activation on pertinent animal traits for YOLOv8(n).

Conclusion

For real-time wildlife monitoring and early warning, the suggested system offers a precise, affordable, and scalable option. Through the integration of YOLOv8(n), Raspberry Pi 5 edge computing, and GSM-based connectivity, the system efficiently functions in distant settings and dramatically shortens reaction times during wildlife infiltration incidents. The findings demonstrate that YOLOv8(n) is the best model for integrated animal detection, enhancing wildlife conservation, lowering agricultural loss, and improving human safety.