Defect detection using weakly supervised learning

In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data.

Embedded light-weight approach for safe landing in populated areas

Landing safety is a challenge heavily engaging the research community recently, due to the increasing interest in applications availed by aerial vehicles. In this paper, we propose a landing safety pipeline based on state of the art object detectors and OctoMap. First, a point cloud of surface obstacles is generated, which is then inserted in an OctoMap. The unoccupied areas are identified, thus resulting to a list of safe landing points. Due to the low inference time achieved by state of the art object detectors and the efficient point cloud manipulation using OctoMap, it is feasible for our approach to deploy on low-weight embedded systems. The proposed pipeline has been evaluated in many simulation scenarios, varying in people density, number, and movement. Simulations were executed with an Nvidia Jetson Nano in the loop to confirm the pipeline's performance and robustness in a low computing power hardware. The experiments yielded promising results with a 95% success rate.