Defect detection of aero-engine blades is a critical technological step to ensure flight safety. Traditional industrial borescope inspection methods rely heavily on manual expertise, which often leads to low efficiency, strong subjectivity, and missed detections of subtle defects. To address these issues, this paper proposes a lightweight and high-precision improved YOLOv11 model, specifically designed for real-time defect detection of engine blades. A high-quality industrial image dataset was constructed, comprising four typical defect types—bending, ablation, cracks, and material loss. Aiming at the characteristics of small targets, complex backgrounds, and multi-scales, a CFES backbone network is constructed to enhance the integration ability of semantic information and reduce the amount of computation. Specifically, ShuffleNetV2 is employed as the backbone network instead of the original one to alleviate computational overhead, while the BiFormer attention mechanism is integrated to strengthen the feature representation capability. Additionally, a Dynamic-DCNv3-based detection head is employed to improve the modeling capability for complex textures and small-sized objects. Experimental results show that the improved model achieves an mAP@0.5 of 85.0%, surpassing the baseline model, while reducing parameters to only 1.7 M. This demonstrates superior detection performance and adaptability for edge deployment. The model was successfully deployed on an industrial borescope platform equipped with the RK3588 chip, where the frame rate remained at approximately 30 frames per second, achieving efficient and stable automatic defect recognition. This study provides a practical solution for intelligent on-site inspection in aviation maintenance and offers technical support for the application of lightweight object detection models in industrial embedded scenarios.