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基于改进YOLOv8算法的误撞输电线路珍稀鸟类智能识别
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基于改进YOLOv8算法的误撞输电线路珍稀鸟类智能识别
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Journal Article
基于改进YOLOv8算法的误撞输电线路珍稀鸟类智能识别
2025
Overview
Q958.1%TP391.4; 为有效防治珍稀鸟类误撞输电线路导致的鸟类伤亡与线路跳闸事故,提出一种基于改进YOLOv8模型的鸟类智能识别方法.根据发生撞线事故的鸟类信息及输电线路周边调研结果,构建了包含11种珍稀鸟类的图像数据集,采用加雾加噪操作进行图像增广,用于模拟真实输电线路场景.通过在YOLOv8网络的特征提取部分加入大型分离卷积注意力模块,减少模型参数量,增强模型对于鸟类特征的提取速度;在特征提取和特征融合网络中增加辅助检测头,增强模型对于鸟类特征的学习能力,进而提高检测性能.算例分析表明,改进模型的平均精度均值、F1分数、FPS分别为95.11%、91.55%、138.89,实现了对于误撞输电线路珍稀鸟类的高效准确识别.后续将模型部署在线路杆塔图像采集系统中,可为珍稀鸟类保护与输电线路运维提供技术支持.
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