Butterfly Detection and Classification Based on Integrated YOLO Algorithm

TL;DR

This paper presents an improved YOLO-based method for automatic butterfly detection and classification, enhancing generalization and small sample recognition capabilities with high accuracy.

Contribution

The study introduces an integrated YOLO algorithm with bio-labeling for better butterfly recognition, addressing small sample challenges and improving detection accuracy.

Findings

High accuracy in butterfly detection

Enhanced generalization ability of YOLO model

Effective recognition with small sample data

Abstract

Insects are abundant species on the earth, and the task of identification and identification of insects is complex and arduous. How to apply artificial intelligence technology and digital image processing methods to automatic identification of insect species is a hot issue in current research. In this paper, the problem of automatic detection and classification recognition of butterfly photographs is studied, and a method of bio-labeling suitable for butterfly classification is proposed. On the basis of YOLO algorithm, by synthesizing the results of YOLO models with different training mechanisms, a butterfly automatic detection and classification recognition algorithm based on YOLO algorithm is proposed. It greatly improves the generalization ability of YOLO algorithm and makes it have better ability to solve small sample problems. The experimental results show that the proposed annotation method and integrated YOLO algorithm have high accuracy and recognition rate in butterfly automatic detection and recognition.