Zero Short Learning for Wildlife Imagery
Abstract
This paper introduces an innovative approach for object detection from wildlife images using Zero-Shot Learning (ZSL) with the YOLO-World model. Unlike previous object detection algorithms, which relied on domain-specific training data, YOLO-World is optimized for zero-shot object recognition, thus recognizing a wide range of categories without explicit training on specific labels. The data for this research have been taken from a dataset pre-processed and pre-trained, already split into sets of training and testing, such that the accuracy in the resulting outcome is more precise. Performance evaluation has been taken with the help of key parameters such as precision, recall, F1-score, Intersection over Union (IoU), mean Average Precision (mAP), and proved the adequacy of the model and its efficiency in detecting highly accurate wildlife objects. The experimental results highlight the better performance of ZSL in the detection of wildlife imagery, with a precision of 0.95 and recall of 0.92, thus achieving a mAP of 0.93 and F1-score of
0.87. A comparative analysis with existing YOLOv3 and YOLOv5 models also highlights the merits of the proposed approach in wildlife recognition tasks.
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