Undercarriage device is one of the essential parts of an aeroplane, and accurate detection of whether the aeroplane undercarriage is operating normally can effectively avoid aeroplane accidents. To address the problems of low automation and low accuracy of small target detection in existing aeroplane undercarriage detection methods, an improved algorithm for aeroplane undercarriage detection YOLO V4 is proposed. Firstly, the convolutional network structure of Inception-ResNet is integrated into the CSPDarkNet53 framework to improve the algorithm’s ability to extract semantic information of target features; then an attention mechanism is added to the path aggregation network algorithm structure to improve the importance and relevance of different features after conceptual operations. In addition, aeroplane and undercarriage datasets were constructed, and finally, the generated partitioned test sets were tested to evaluate the test performance of Faster R-CNN, YOLO V3, and YOLO V4 target detection algorithms. The experimental results show that the improved algorithm has significantly improved the recall rate and the mean accuracy of detection for small targets in our dataset compared with the YOLO V4 algorithm. The reasonableness and advancedness of the improved algorithm in this paper are effectively verified.