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The feasibility of deploying object detection to interpret the environment is questioned in several mission-critical applications leading to raised concerns about the ability of object detectors in providing reliable and safe predictions of the operational environment, regardless of weather and light conditions. The lack of a comprehensive dataset, which causes class imbalance and detection difficulties of hard examples, is one of the main reasons of accuracy loss in attitude safe object detection. Data augmentation, as an implicit regularisation technique, has been shown to significantly improve object detection by increasing both the diversity and the size of the training dataset. Despite the success of data augmentation in various computer vision tasks, applying data augmentation techniques to improve safety has not been sufficiently addressed in the literature. In this paper, we leverage a set of data augmentation techniques to improve the safety of object detection. The aircraft in-flight image data is used to evaluate the feasibility of our proposed solution in real-world safety-required scenarios. To achieve our goal, we first generate a training dataset by synthesising the images collected from in-flight recordings. Next, we augment the generated dataset to cover real weather and lighting changes. Introduction of artificially produced distortions is also known as corruptions and has since recently been an approach to enrich the dataset. The introduction of corruptions, as augmentations of weather and luminance in combination with the introduction of artificial artefacts, is done as an approach to achieve a comprehensive representation of an aircraft’s operational environment. Finally, we evaluate the impact of data augmentation on the studied dataset. Faster R-CNN with ResNet-50-FPN was used as an object detector for the experiments. An AP@[IoU=.5:.95] score of 50.327% was achieved with the initial setup, while exposure to altered weather and lighting conditions yielded an 18.1% decrease. The introduction of the conditions into the training set led to a 15.6% increase in comparison to the score achieved from exposure to the conditions.