Evacuation slides are critical aircraft safety components governed by stringent regulatory standards set by agencies like the Federal Aviation Administration and European Union Aviation Safety Agency. To comply with these standards, the maintenance and repacking of slides, currently performed manually, require operators to follow hundreds of precise steps. This process is labor-intensive, error-prone, and can result in costly delays and safety risks due to human error. Real-time visual inspection systems can help address these challenges, however, a key obstacle for real-world deployment of such systems is the scarcity of benchmark data from aerospace factory operations needed to validate and verify their performance. To enable this, we introduce the first known dataset tailored for evacuation slide inspection, comprising over 14,500 images captured under real and controlled conditions. This data aims to capture slide folding procedures of Embraer AFT evacuation slides, such that developed real-time systems can: (1) estimate the occluded position of the Pressure Relief Valve (PRV), (2) detect context-sensitive foreign objects such as packing clamps, and (3) calculate slide fold dimensions to prevent tolerance stacking errors. From this dataset, five benchmarks were constructed to evaluate performance across the three requirements. Baseline models were developed, including a PRV localization network using LSTMs, a variational autoencoder and object detection pipeline for packing clamp FOD, and a depth and reference-based slide fold measurement calculation method. When tested on our benchmarks, the depth-based measurement estimator showed precision and accuracy, the clamp FOD methodology showed high precision for images taken from specific cameras, however, the PRV position estimation remains a challenge that requires further research. Overall, our results set a foundation for the automation of visual inspection in slide packing and offer benchmarks for future research in this safety-critical inspection task.

Adversarial training and its variants have become the standard defense against adversarial attacks - perturbed inputs designed to fool the model. Boosting techniques such as Adaboost have been successful for binary classification problems, however, there is limited research in the application of them for providing adversarial robustness. In this work, we explore the question: How can AdaBoost ensemble learning provide adversarial robustness to white-box attacks when the "weak" learners are neural networks that do adversarial training? We design an extension of AdaBoost to support adversarial training in a multiclass setting, and name it Adven. To answer the question, we systematically study the effect of six variables of Adven’s training procedure on adversarial robustness. From a theoretical standpoint, our experiments show that known characteristics from adversarial training and ensemble learning apply in the combined context. Empirically, we demonstrate that an Adven ensemble is more robust than a single learner in every scenario. Using the best found values of the six tested variables, we derive an Adven ensemble that can defend against 91.88% of PGD attacks and obtain 96.72% accuracy on the MNIST dataset.