Evaluation of a Harmful-Animal Detection Method Combining Techniques for Object Detection and Skeleton Estimation
DOI:
https://doi.org/10.22452/mjs.vol45sp2.3Keywords:
Image Recognition, Object Detection, Skeleton Estimation, YOLOv8, AP-10KAbstract
This paper presents a new harmful-animal detection method that combines conventional algorithms for image recognition with skeleton estimation techniques. In many places around the world, wild animals encroach on human territory, causing personal injury and damage to crops. One way to address this social problem is to implement a harmful-animal detection system using cameras and object detection algorithms; however, current methods face the challenge of low detection accuracy, limiting their practical application. This study aims to address this challenge through a novel approach that combines conventional algorithms for image recognition with skeleton estimation techniques. The proposed method uses transfer learning on two pre-trained models: YOLOv8—known for its high accuracy and real-time performance—and HRNet, a widely used model for animal skeleton estimation. For training, a dataset extracted from AP-10K was used. In the proposed method, animals in the input image are detected by combining the inference results from two image recognition algorithms. The initial result is obtained from the input image itself, while the subsequent result is derived from a skeleton image created by plotting estimated keypoints and connecting lines. The final inference result is weighted and combined to improve the accuracy of animal detection in the image. The proposed method was evaluated by comparing its detection results with those of a conventional detection method while varying the minimum confidence threshold (conf). The evaluation demonstrated that the proposed method performs as well as or better than the conventional detection method. Furthermore, the proposed method exhibited greater overall stability, which simplified the process of conf parameter adjustment, making it more user-friendly. These findings suggest that the proposed method can contribute to the development of a more practical and effective harmful-animal detection system.
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