Low-activity hotspot investigation method via scanning using deep learning - Bae, Junwoo; Min, Sujung; Seo, Bumkyoung; Roh, Changhyun; Hong, Sangbum (Frontiers in Energy Research, 2022)
Small areas of elevated activity are a concern during a final status scan survey of residual radioactivity of decommissioned and contaminated sites. Scanning surveys are typically used to identify small area of hotspots with low radioactivity, and small hotspots are difficult to distinguish during scanning surveys of contaminated site. Due to the characteristics of scanning, the lower limit of detection is relatively high because the number of counts is low due to the short measurement time. To overcome this, an algorithm capable of finding hotspots with little information through deep learning was developed. An ANN (Artificial Neural Network) model was trained with scan survey data acquired from a Monte Carlo-based computational simulation. A random mixing method was used to obtain sufficient training data. In order to respond properly to the experimental data, training and verification were conducted in various situations, in this case in the presence or absence of random background counts and collimators and various source concentrations. Experimental data were obtained using a conventional detector, in this case the 3″ × 3″ NaI(Tl). Results were well predicted even in cases at less than 1 Bq/g, which is lower than the scan MDC (Minimum Detectable Concentration) of the detection system. The source position and size are also important in residual radioactive evaluations and scanning data images were evaluated in ANN modes with suitable prediction results. The proposed method can be applied to identify elevated activity through scanning surveys of sites and building surfaces of decommissioned facilities.