The visual perception function based on machine learning is crucial for enhancing situational awareness or autonomous flight capabilities of aircraft in complex environments， and its performance has significant impact on flight safety. However， the inherent probabilistic nature of machine learning techniques poses substantial challenges to meeting airworthiness safety objectives， thereby hindering their application in airborne systems. To address this issue， a robustness-oriented design method for airborne visual perception based on deep ensemble learning is established. First， a highly representative dataset is generated based on the operational design domain， and a K-fold cross-validation method based on CW-SSIM is proposed to improve the independence between the training and validation sets with limited data. Second， based on the YOLO architecture， depthwise separable convolution is introduced， and three optimized base learners are designed to address different detection needs through multi-scale feature fusion， enhanced focus on small object detection， and fine-grained feature extraction. Finally， an ensemble learning method is designed using a weighted adaptive fusion strategy to dynamically adjust the weights of base learners， thereby improving the model accuracy and robustness. Experimental results show that the ensemble learning model outperforms detection box fusion algorithms such as NMS and WBF. When the IoU is not less than 0.7， the ensemble model improves the average P-value， R-value， and F1 score by at least 11.36%， 2.06%， and 6.78%， respectively， compared to a single model. When the IoU is no less than 0.75， the AP value increases by at least approximately 3%. These results indicate that proposed method significantly enhances target detection accuracy and robustness in complex environments， effectively reducing false positives and missed detections， and provides technical assurance for the safe flight of aircraft.