Radioactive hot-spot localisation and identification using deep learning

The detection of radioactive hot-spots and the identification of the radionuclides present have been a challenge for the security sector, especially in situations involving chemical, biological, radiological, nuclear and explosive threats, as well as naturally occurring radioactive materials. This work proposes a solution based on Machine Learning techniques, with a focus on artificial neural networks (NNs), in order to localise, quantify and identify radioactive sources. Firstly, the created RHLnet model uses observations of radiological intensity counts and corresponding localisations to estimate the number, location and activity of unknown radioactive sources present in a given scenario. Then, another model (RHIdnet) gets the gamma spectrum of the sources to perform the identification of the corresponding radionuclides. For this, a training data set composed of simulated data is used during the training process, and so, using algorithms with the models already trained, fast and accurate predictions are achieved, ensuring the reliability of such a NN-based approach. The proposed solution is tested in simulated and real scenarios, with multiple sources, providing a low number of limitations, related to possible false negatives and false positives. Besides, the results have shown that the algorithm is scalable for very large regions, as well as for very small scenarios. Single and multiple isotope identification on each sample is explored, highlighting the benefits as well as possible improvements. Thus, NNs have demonstrated the capability of being an emerging tool with the potential to make a difference in the nuclear field, by helping in the development of novel techniques and new solutions in order to safeguard human lives.