南京航空航天大学学报（自然科学版）2026，Vol.58Issue(2)：372-379,8.DOI:10.16356/j.2097-6771.2026.02.013
BD-YOLO:一个基于深度学习的航空发动机孔探图像叶片损伤检测模型
BD-YOLO:A Deep Learning-Based Model for Blade Damage Detection in Aero-engine Borescope Images
摘要
Abstract
To address the issues in aero-engine borescope images,such as the arbitrary orientation of blade damage and the tendency for slender damage to introduce excessive background interference,leading to reduced localization accuracy,this paper proposes a rotated object detection model,BD-YOLO,based on an improved You Only Look Once version 8(YOLOv8).Firstly,a small object detection module named cross stage partial receptive field enhancement module(CSRFEM),which integrates the cross stage partial(CSP)and receptive field enhancement module(RFEM),is designed to enhance feature extraction capabilities for minor damages.Secondly,an improved bidirectional feature pyramid network,SimBiFPN,is introduced into the neck network to achieve efficient multi-scale feature fusion.Finally,a dedicated small object detection head is added to the head network to improve the recognition accuracy of small-sized damages.Experimental results demonstrate that BD-YOLO achieves mean average precision(mAP)50,mAP75,and mAP50-95 values of 98.6%,84.3%,and 63.3%,respectively,with a detection speed of 34 frames per second,enabling high-precision real-time detection of blade damage.关键词
航空发动机/孔探检测/YOLOv8/叶片损伤/目标检测Key words
aeroengine/borescope detection/you only look once version 8(YOLOv8)/blade damage/object detection分类
航空航天引用本文复制引用
徐超,王文哲,蒋增华,冷晟,王静秋..BD-YOLO:一个基于深度学习的航空发动机孔探图像叶片损伤检测模型[J].南京航空航天大学学报（自然科学版）,2026,58(2):372-379,8.基金项目
直升机动力学全国重点实验室2024年度科技创新基金(KY-1003-2024-0027). （KY-1003-2024-0027）
