BD‑YOLO: A Deep Learning‑Based Model for Blade Damage Detection in Aero‑engine Borescope Images

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.