Monitoring of the marine environment requires large amounts of data, simply due to its vast size. Therefore, underwater autonomous vehicles and drones are increas- ingly deployed to acquire numerous photographs. How- ever, ecological conclusions from them are lagging as the data requires expert annotation and thus realistically can- not be manually processed. This calls for developing au- tomatic classification algorithms dedicated for this type of data. Current out-of-the-box solutions struggle to provide optimal results in these scenarios as the marine data is very different from everyday data. Images taken under water dis- play low contrast levels and reduced visibility range thus making objects harder to localize and classify. Scale varies dramatically because of the complex 3 dimensionality of the scenes. In addition, the scarcity of labeled marine data pre- vents training these dedicated networks from scratch. In this work, we demonstrate how transfer learning can be uti- lized to achieve high quality results for both detection and classification in the marine environment. We also demon- strate tracking in videos that enables counting and measur- ing the organisms. We demonstrate the suggested method on two very different marine datasets, an aerial dataset and an underwater one.
We focus on mitigating strong interferences that can jam underwater acoustics emissions aimed for detection or communications. We consider two types of interferences: narrowband like continuous waveform (CW) and wideband like chirp. Both types are assumed to be strong, such that, without interference cancellation, performance is poor. We offer two interference canceling algorithms, each corresponding to a different interference type. The two algorithms are designed to mitigate strong interference, while maintaining the desired signal intact. These algorithms can be executed sequentially to manage both types of interference simultaneously. Our solution takes advantage of the sparsity of the underwater acoustic channel, as well as the assumed correlation of the interference signals. Numerical simulations, as well as results from a sea experiment, show that our algorithms significantly reduce the effect of strong interferences for the fast time-varying and long-delay spread underwater acoustic channel.