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.

To address the airworthiness safety challenges caused by inherent uncertainty outputs of machine learning technology in airborne systems, a system safety assessment method based on the generalization theory is proposed for CNN classification under the framework of SAE ARP4761standards. First, based on the PAC-Bayes theory, the training method is improved through conditional gaussian process to optimize the generalization boundand obtain a quantified representation of the uncertainty of the CNN model. Second, an integration method for software uncertaintyand hardware reliability based on generalization bound confidence is proposed to obtain comprehensive failure basic data of CNN components, supporting quantitative safety assessment of the aircraft/system. Finally, taking the airborne GNSS interference signal recognition module based on CNN as a case, the proposed method is shown to be effective in safety assessment, and is also experimentally verified that the generalization boundary based on conditional gaussian process has a tighter computational boundary than that of ordinary PAC-Bayesand Vapnik-Chervonenkis dimensions.

To address the airworthiness safety challenges brought by the application of reinforcement learning in UAV intelligent avoidance systems, this paper proposes a safety assessment method for the intelligent avoidance system based on Bayesian optimization theory within the framework of the SAE ARP4761 standard. First, the intelligent avoidance system model is established based on the UAV kinematic model and the Proximal Policy Optimization (PPO) algorithm. Second, by integrating the system model verification task with Bayesian optimization theory, the iterative training of the Gaussian surrogate model is achieved through three acquisition functions: uncertainty exploration, boundary refinement, and failure region sampling. This enables safety verification, safety boundary determination, and functional failure probability analysis of the intelligent avoidance system with a small number of samples, supporting quantitative safety assessment at the whole aircraft/system level. Finally, taking a typical intelligent avoidance system architecture as a case, the proposed method is demonstrated to effectively support airworthiness safety assessment, providing essential airworthiness compliance methods and technical guarantees for the deployment of intelligent avoidance systems. Experimental results further validate that, under limited sample conditions, the Bayesian optimization-based method outperforms uniform sampling and Monte Carlo methods by offering more detailed failure boundary predictions, precise failure probability estimation, and higher confidence levels for the reinforcement learning module.