Abstract :

Wildlife–human And Wildlife–vehicle Interactions Pose Serious Risks To Both Public Safety And Animal Conservation, Particularly In Regions Monitored Using Large-scale Camera Trap Systems. Manual Inspection Of Such Image Data Is Inefficient, Error-prone, And Impractical For Real-time Applications. To Address These Limitations, This Work Proposes An Accurate And Fast Animal Species Detection System Based On Deep Learning Techniques. The Proposed Framework Employs A Cascaded Convolutional Neural Network (CNN) Architecture Designed To First Distinguish Between Humans And Animals, Followed By Precise Classification Of Animal Species. Unlike Traditional Approaches Relying On Handcrafted Features Or Small Datasets, The System Is Trained Using Large, Labeled Datasets Collected From The British Columbia Ministry Of Transportation And Infrastructure (BCMOTI) And The Snapshot Wisconsin Project, Enabling Robust Learning Under Diverse Environmental Conditions Such As Low Illumination, Occlusion, And Partial Visibility. The Proposed Mechanism Incorporates Systematic Image Preprocessing, Feature Extraction Through Multiple Convolutional Layers, And Optimized Classification Using Softmax-based Decision Functions. Confidence-based Prediction Filtering Is Also Applied To Enhance Reliability By Suppressing Low-confidence Outputs. Experimental Evaluation Demonstrates That The System Achieves High Detection Accuracy While Maintaining Reduced Computational Complexity, Making It Suitable For Largescale And Near Real-time Deployment. The Results Confirm That The Proposed Approach Significantly Improves Efficiency, Scalability, And Classification Performance, Providing A Practical And Automated Solution For Intelligent Wildlife Monitoring And Collision Prevention Systems. Keywords – Animal Species Detection, Convolutional Neural Network (CNN), Deep Learning, Wildlife Monitoring, Camera Trap Images, Object Classification, Human–animal Interaction, Image Processing, Machine Learning, Intelligent Surveillance

Published:

20-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

58-63

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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