Abstract: Aircraft landing gear safety pin is a kind of aircraft safety protection device.Before takeoff,it should be ensured that the safety pin is pulled out,so as to protect the safety of aircraft flight.The traditional aircraft landing gear safety pin inspection method is based on manual patrol,which usually produces safety hazards due to human factors and is also inefficient.In order to solve this problem,deep learning-based target detection algorithms are applied to aircraft landing gear safety pin inspection for the first time and optimised in terms of lightweight and performance of the algorithm model to better meet the inspection task in terms of arithmetic resources,storage resources and algorithm performance.Based on the industrial-grade deep learning target detection model YOLOv5,the model is improved in terms of lightweighting while MobileNetV3 is introduced as the backbone network for feature extraction,which greatly reduces the parameters and the GFLOPs while ensuring the accuracy,and in terms of algorithmic performance,the lightweight coordinate attention module is inserted to help the algorithmic network to locate the target more accurately and to improve the accuracy of target detection.Experimental results show that the improved YOLOv5 model can effectively perform the aircraft landing gear safety pin detection task.Compared with the pre-optimization model,the mAP is increased by 2.5%,F1 score is increased by 1.4%,and the parameter is reduced by 50%,GFLOPs are reduced by 61%.The algorithm can provide a reference for automatic aircraft landing gear safety pin detection methods.

Key words: Landing gear safety pin, Deep learning, Target detection, YOLOv5, Coordinate attention