The ability to track and process large images and focus only on areas of interestis extremely important to reduce computational resources, which translates toa decrease in costs, when working on the cloud, or energy, when working withedge computing devices. A clear remote sensing task that can benefit from suchreduction consists in detecting ships in open seas. In particular, in such scenariothousands of miles may only contain water without ships. In this work, wepropose an efficient cascade architecture that could be deployed in any machine.To achieve our goal, current state-of-the-art image-based machine learningalgorithms will be assessed, building on top of previous related work.