ADVANCED WILD ANIMAL DETECTION AND ALERT SYSTEM USING THE YOLO V5 MODEL POWERED BY AI

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Dr. V. NAGAGOPIRAJU
SUVARNA PINNINTI
ANJAMMA TAMMA
SAI TEJA KAJJAYAM
KALESHAVALI KAKARLA

Abstract

An advanced wild animal detection and alert system using you only look once version5 (YOLO V5) model. The system utilizes you only look once version5 (YOLO V5) object detection algorithm to identify wild animals and alert users to their presence in real-time. The system employs a camera to capture real-time video, which is then sent to a computer running you only look once version5 (YOLO V5) algorithm. When the system detects a wild animal, it sends an alert to the wild animal by playing any sounds like bullets firing. The system is expected to have a significant impact on the safety of people in areas with high wildlife populations. This advanced wild animal detection and alert system using you only look once version5 (YOLO V5) model has the potential to improve the safety of people in areas with high wildlife populations. Future work will focus on improving the accuracy of the system and implementing it in real-world scenarios.

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How to Cite
NAGAGOPIRAJU, D. V. ., PINNINTI, S. ., TAMMA, A. ., KAJJAYAM, S. T. ., & KAKARLA, K. . (2024). ADVANCED WILD ANIMAL DETECTION AND ALERT SYSTEM USING THE YOLO V5 MODEL POWERED BY AI. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(1), 142–145. https://doi.org/10.61841/turcomat.v15i1.14556
Section
Research Articles

References

Shivang A, Jean O D T and Frédéric D 2018. J. Recent Advances in Object Detection in theAgeof Deep

Convolutional Neural Networks. arXiv e-prints arXiv1809.03193.

Ross G 2015. J. Fast R-CNN. 2015 IEEE International Conference on Computer Vision. 1440-1448.

Carranza-García M, Torres-Mateo J, Lara-Benítez P and García-Gutiérrez J 2021. J. OnthePerformance of OneStage and Two-Stage Object Detectors in Autonomous Vehicles UsingCamera Data. Remote Sens. 13(1): 89.

Kanlayanee K, Siranee N and Joshua M P 2021. J. Open source disease analysis systemof cactus by artificial

intelligence and image processing. The 12 th International ConferenceonAdvances in Information Technology.

Delong Q, Weijun T, Qi Y and Jingfeng L 2021. B. YOLO5Face: Why Reinventinga FaceDetector. ECCV

Workshops. [6] Joseph R, Santosh D, Ross G and Ali F 2015. J. You Only Look Once: Unified, Real-TimeObject

Detection. 2016 IEEE International Conference on Computer Vision and PatternRecognition. 779-788

Joseph Rand Ali F 2018. J. YOLOv3: An Incremental Improvement. arXiv e-printsarXiv1804.02767

Alexey B, Chien-Yao W and Hong-Yuan M L 2020. J. YOLOv4: Optimal Speed and Accuracyof Object Detection.

arXiv e-prints arXiv2004.10934

Hu J, Zhi X, Shi T, Zhang W, Cui Y and Zhao S. 2021, J. PAG-YOLO: APortableAttention-Guided YOLO

Network for Small Ship Detection. Remote Sensing. 13(16):3059

Hou Y, Yang Q, Li L and Shi G 2023. J. Detection and Recognition Algorithmof Arbitrary-Oriented Oil

Replenishment Target in Remote Sensing Image. Sensors (Basel). 23(2):767.

Munhyeong K, Jongmin J, and Sungho K 2021. J. ECAP-YOLO: Efficient Channel AttentionPyramid Yolo for

Small Object Detection In Aerial Image. Remote Sensing, 13(23):4851.

Madodomzi M, Philemon T, Tsungai Z and Abel R 2021. J. On the Performance of One-Stageand Two-Stage

Object Detectors in Autonomous Vehicles Using Camera Data. Remote Sens. 13(1):89.