Efficient Wildlife Intrusion Detection System using Hybrid Algorithm

Human-wildlife conflict arises when the needs and behavior of animals have a detrimental influence on humans or when humans have a negative impact on the needs of wildlife. The primary causes of Man-Wildlife Conflicts include agricultural expansion, human settlement, livestock overgrazing, deforestation, illegal grass gathering, and poaching. Each year, human-animal conflict in human habitats causes a massive loss of sources and put lives in jeopardy. As the global human population continues to force wildlife out of their natural habitats, conflicts are unavoidable, which is why habitat loss is one of the most prevalent dangers to endangered animals. So, it is necessary to detect animals and identify the animal detected to reduce the effects of human-animal conflict. This research study has developed a hybrid algorithm, which classifies animal images into multiple groups using YOLO v5 (You only look once) combined with CNN. The proposed system distinguishes whether the animal is in human environment or not, and then reliably distinguishes which animal class it belongs to using CNN. The model has been tested through its paces on a variety of tasks in analysis to define how well it performs in various scenarios. The system is being fine-tuned with the goal of attaining the most accurate results possible in recognizing and decreasing hazards posed by animal invasions into human land. These experimental findings show that the yolov5 coalescing technique paired with CNN can properly categorize animals in habitats, with a 92.5% accuracy from the proposed model.