辽宁工程技术大学学报（自然科学版）2024，Vol.43Issue(5)：625-632,8.DOI:10.11956/j.issn.1008-0562.20240196
多尺度特征融合增强检测模型MFFE-YOLO
A multi-scale feature fusion enhanced detection model MFFE-YOLO
摘要
Abstract
In order to solve the problems of weak detection ability of small target defects of power equipment,high false detection and missed detection rate,and insufficient shallow network semantic information in traditional inspection image detection methods,a feature fusion enhanced detection method for small target defects of power equipment(multi-scale feature fusion enhanced you only look once,MFFE-YOLO)is proposed.This method designs a multi-scale feature fusion enhancement mechanism(multi-scale feature fusion enhancement,MFFE),which can capture target features more comprehensively.The study shows that embedding the cross-space learning multi-scale attention mechanism EMA and FasterNet Block in the C2f-EF module can optimize the operation efficiency of the model;the average accuracy,parameter amount and frame rate indicators of the MFFE-YOLO method are better than that of other methods,and can achieve a good balance between high accuracy and real-time performance.关键词
电力巡检/电力设备缺陷/小目标检测/特征融合增强/YOLO/多尺度特征Key words
power inspection/power equipment defects/small target detection/feature fusion enhancement/YOLO/multiscale feature分类
信息技术与安全科学引用本文复制引用
彭继慎,马龙泽,孙梦宇,刘金龙..多尺度特征融合增强检测模型MFFE-YOLO[J].辽宁工程技术大学学报（自然科学版）,2024,43(5):625-632,8.基金项目
国家自然科学基金项目(BK546513248) （BK546513248）
