This work presents a dataset with surveillance imagery over the sea captured by a small size UAV. This dataset presents object examples ranging from cargo ships, small boats, life rafts to hydrocarbon slick. This dataset presents scenarios that range from the observation of cargo ships, small boats and life rafts, to a hydrocarbon slick. The video sequences were captured using different types of cameras, at different heights and different perspectives. The dataset also contains thousands of labels with positions of objects of interest. This was only possible to achieve with the labeling tool also described in this work. Additionally, using standard evaluation frameworks, we establish a baseline of results using algorithms developed by the authors which are better adapted to the maritime environment.

In this paper, we study the effectiveness of learning temporal features to improve detection performance in videos captured by small aircraft. To implement this learning process, we use a convolutional long short-term memory (LSTM) associated with a pretrained convolutional neural network (CNN). To improve the training process, we incorporate domain-specific knowledge about the expected size and number of boats. We carry out three tests. The first searches the best sequence length and subsampling rate for training and the second compares the proposed method with a traditional CNN, a traditional LSTM, and a gated recurrent unit (GRU). The final test evaluates our method with the already published detectors in two data sets. Results show that in favorable conditions, our method's performance is comparable to other detectors but, on more challenging environments, it stands out from other techniques.

In order to detect special nuclear materials and other radioactive materials in Security and Defense scenarios, normally, a combination of neutron and gamma-ray detection systems is used. In particular, to avoid illicit traffic of special nuclear materials and radioactive sources/materials, radiation portal monitors are placed at seaports to inspect shipping-container cargo. Despite their large volume (high efficiency), these detection systems are expensive, and therefore only a fraction of these containers are inspected. In this work, a novel mobile radiation detection system is presented, based on an EJ-200 plastic scintillator for the detection of gamma rays and beta particles, and a neutron detector EJ-426HD plastic scintillator (with ⁶Li) embedded in a compact and modular moderator. The use of silicon photomultipliers in both detectors presented advantages such as lightweight, compactness, and low power consumption. The developed detection system was integrated in a highly maneuverable multirotor. Monte Carlo simulations were validated by laboratory measurements and field tests were performed using real gamma-ray and neutron sources. The detection and localization within one meter was achieved using a maximum likelihood estimation algorithm for ¹³⁷Cs sources (4 MBq), as well as the detection of ²⁴¹Am–beryllium (1.45 GBq) source placed inside the shipping container.

In this paper we present a sea vessel detection algorithm in aerial image sequences acquired by an unmanned aerial vehicle. The proposed method is robust to variable background lighting, highlights due to sun reflections, vehicle self motion and scale changes. By relying in simple blob analysis rules, based on both spatial and temporal constraints, the algorithm is capable of real-time operation onboard the vehicle, even with non optimized code. We evaluate our method on three sequences labeled with ground truth vessel position, with more that 2900 frames. Overall we are able to achieve very low false positive rates even in heavy sun reflection conditions.