Aircraft landing gear defect detection is of great significance to ensure flight safety. Aiming at the problems of insufficient accuracy and large number of parameters in the existing target detection methods, an aircraft landing gear defect detection method named GSV-YOLO is proposed, which is based on YOLOv7-tiny and improved. Firstly, Ghost convolution is used to replace the standard convolution in the YOLOv7-tiny model to realize the model lightweight and reduce the number of parameters; secondly, a depth-separable self-attention module (SepViT) is introduced to enhance the model′s ability of extracting the global information and to reduce the misdetection and underdetection of small target samples; a lightweight detector head is designed to strengthen the model′s classification and localization ability; finally, the dynamic non-monotonic focusing mechanism based on Wise-IoU loss to focus on common quality anchor frames to further enhance the detection performance. The experimental results show that GSV-YOLO achieves an average detection accuracy of 80.4% on the aircraft landing gear defect dataset, which is 4.9% higher compared to the original model, and the amount of model parameters is reduced by 10.6%, which improves both the model scale and detection accuracy. Applying this method to industrial environments significantly improves the detection efficiency and has great application value.