Accurate detection of surface defects on aircraft blades is crucial for ensuring the safe and reliable operation of aero- engines.Currently,vision-based algorithms for detecting surface defects on aircraft blades suffer from poor real-time performance,high missed detection rates,and inaccurate target localization.To address these issues,this paper proposes an aircraft blade surface defect detection algorithm based on deep neural networks.To improve detection real-time performance,we design the depthwise separable convolution (DSC) model to decompose standard convolutions.To reduce missed detection of small defect targets,we propose the squeeze-and-excitation path aggregation network (SE-PAN) model to recalibrate the features of each channel,allowing features with stronger information to receive more attention.To enhance localization accuracy,we design the focal-distance intersection over union (Focal-DIOU) loss function to mitigate the effect of inefficient boxes.Experimental results on our aircraft blade surface defect dataset demonstrate that our algorithm achieves Precision,Recall and AP of 95.7%,94.6% and 96.3%,respectively,with a detection frame rate of 24 frames per second,all of which outperform mainstream detection algorithms.