Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

This letter presents the experimental results obtained for an automatic predetection of small ships (about 5$\times$5 pixels) in high-resolution optical satellite images. Our images are panchromatic SPOT 5 images, whose resolution is 5 m per pixel. Our detection method is based on the Bayesian decision theory and does not need any preprocessing. Here, we describe the method precisely and the tuning of its two parameters, namely, the size of the analysis window and the threshold used to make a decision. Both are fixed from the receiver operating-characteristic curves that we draw from different sets of tests. Finally, the overall results of the method are given for a set of images, as close as possible to the operational conditions.