Comparative Analysis of Object Detection Models for Peacock Detection: Evaluating Their Performance and Key Points

Abstract

Peacocks being a major contributor in the damage caused in the agriculture sector, an imminent requirement of efficient detection system for timely intervention. Object Detection being an important application of Computer Vision aims in precisely finding the location and identifying the object in images and videos. With increased advancements in the deep learning field and with various models, choosing the best model that not only performs accurate object detection but is also evaluated based on its inference time. This research aims in conducting an analysis on various models in market on the application of peacock detection evaluating them based on accuracy, precision, recall and F1-score. The Yolo11 yields the highest result with the accuracy of 84.9%. The detailed comparison with various evaluation metrics gives the efficient solution in mitigating this problem in-hand saving the agriculture from further damage and incurring future losses.