Advancing Ecological Research with MobileNet-SSD V2 CNN: A Leap in Machine Learning

Across different regions, the ongoing struggle between humans and animals takes various shapes, including the prevalent problems with tigers in urban spaces and crop destruction by wild elephants. Crafting successful approaches to mitigate these human-wildlife confrontations stands as a pressing issue worldwide. Manually analyzing a large volume of photos and collected footage is exceptionally time-consuming, labor-intensive, and costly. Recent advancements in deep learning techniques have demonstrated promise in the identification of objects and species within images. In this paper, an automated wildlife detection system that leverages computer vision techniques and machine learning methods is introduced where the primary goal is to validate and train a Convolutional Neural Network (CNN), specifically MobileNet-SSD V2, to detect wildlife captured by camera traps. The approach includes building a versatile CNN framework with labeled images from benchmark datasets, annotated using LabelImg, and implementing it in TensorFlow Lite. Regular updates with fresh camera-trap images will enhance species recognition precision. This method could aid in conserving endangered species.