Aircraft blade surface defect detection based on deep neural net-works
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.
defect detectiondeep neural networkloss functionattention model
NETL
NSTL
万方数据
