Addressing the challenges of low efficiency and potential oversight in artificial detection of surface defects on aero-engine blades

this paper introduces a novel lightweight intelligent defect detection model

termed YOLOv5-GA. The model incorporates a GhostConv module and C3Ghost into the backbone network to minimize parameters and computational load

thereby enhancing its lightweight nature. Furthermore

the integration of an asymptotic feature pyramid network (AFPN) into the neck network enhances the model’s capability to detect small targets. Experimental findings demonstrate that in the domain of aircraft engine blade defect recognition

the proposed algorithm not only achieves an mAP of 92.6%

a 4.6-percentage-point enhancement over the baseline network but also reduces the model size to a mere 9 MB

reflecting a 38% reduction compared to the baseline. Additionally

on the NEU-DET dataset

the model achieves an mAP of 77%

outperforming other networks while significantly reducing model size. Thus

the proposed network boasts lightweight

efficient

and reliable characteristics

facilitating the effective detection of critical defects in aero-engines.