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.