Abstract: To enhance the accuracy and robustness of defect detection in aerospace engines, this paper proposes an improved YOLOv8-based algorithm for detecting aerospace material defects. Aiming to address the challenges associlated with extracting surface defect features from aerospace materials, such as low target distinctiveness and scale variation, a Global Channel and Spatial Attention (GCSA) module is designed and integrated into YOLOv8. The GCSA module combines channel attention, channel shuffle, and spatial attention mechanisms to strengthen the model’s capability in modeling global contextual dependencies, thereby effectively capturing subtle defects and irregular-shaped damages. Experimental results on a self-built aerospace defect dataset demonstrate that the improved algorithm achieves a 1.5% increase in mAP@0.5 and a 1.1% improvement in mAP@0.5:0.95 compared to the original YOLOv8 model. The proposed algorithm maintains an optimal balance between detection accuracy and speed, significantly enhancing the capability to identify defects in critical aerospace engine components. This work provides a more reliable solution for defect detection tasks in complex industrial scenarios.