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.