Abstract: In response to the problems of slow detection speed, low accuracy, complex image background, difficulty in distinguishing background and target, slow convergence speed and low efficiency in the prediction process of existing object detection algorithms in remote sensing image aircraft target detection, this paper adopts two optimization strategies, namely the introduction of attention mechanism and replacement loss function. Based on the YOLOv5 algorithm model, to improve the algorithm's detection performance for aircraft targets, the training results on the DOTA dataset show that by introducing the CBAM attention mechanism in the C3 module of the YOLOv5 network architecture backbone, the algorithm's detection performance has been significantly improved. Among them, the accuracy of the model training results P has been improved by 6%, the recall rate R has been improved by 2%, and the average precision(mAP) value has been improved by 2.8%. In the YOLOv5 prediction process, Focal EIoU and SIoU loss functions are used to replace the original CIoU loss functions. The experimental results show that the improved algorithm model significantly improve the regression accuracy, among which the model optimized using SIoU loss function have the best effect. The precision(P) of the model training results increased by 4.3%, the recall rate R increased by 2%, and the average precision(mAP) value increased by 2.7%. The improved YOLOv5 algorithm provides a reference for achieving high-precision real-time detection of aircraft targets.

Key words: object detection, aircraft inspection, YOLOv5, attention mechanism, loss function