Increase in Patent Applications for Radiation-Based Cargo Inspection Combined with AI

Example material of AI-based X-ray image automatic reading system process. Provided by the Korean Intellectual Property Office.

[Asia Economy (Daejeon) Reporter Jeong Il-woong] Industry interest in radiation inspection technology has recently led to an increase in patent applications related to cargo inspection combined with artificial intelligence technology.

According to the Korean Intellectual Property Office on the 7th, a total of 143 patent applications related to cargo inspection using radiation such as X-rays were recorded from 2000 to 2019. Among these, 58 applications were filed between 2015 and 2019, showing an increase nearly double the number of applications (31) filed in the previous five years (2010-2014).

From 2010 to 2019, the trend of patent applications by inspection target showed that personal baggage accounted for the highest proportion with 45 cases (31%), followed by large containers with 38 cases (27%), vehicles and vehicle-loaded cargo with 18 cases (13%), and general small to medium-sized cargo with 16 cases (11%).

During the same period, patent applications by applicants were led by foreigners with 73 cases (51%), domestic companies with 28 cases (20%), domestic research institutes with 26 cases (18%), domestic individuals with 12 cases (8%), and domestic universities with 3 cases (2%).

By type of radiation used and inspection method in cargo inspection, inspection methods using only X-rays accounted for the majority with 127 cases (89%), methods irradiating neutrons, gamma rays, etc. accounted for 12 cases (8%), and methods detecting radiation such as X-rays and gamma rays emitted from the inspection target accounted for 4 cases (3%).

Recently, there has also been a notable increase in patent applications combining artificial intelligence-related technology with radiation-based cargo inspection.

Representative technologies include ▲ learning X-ray images of baggage with a self-learning AI engine and then identifying items similar to previously learned hazardous items during actual baggage inspection ▲ using artificial neural networks to compare extracted cargo image information with textual information on the cargo manifest to determine cargo passage ▲ and determining passenger passage by using physical features such as body movements acquired through deep learning technology along with passenger belongings inspection.

Im Hae-young, head of the Measurement Technology Examination Team at the Korean Intellectual Property Office, said, “As the scale of cargo transportation increases due to the activation of non-face-to-face transactions, the speed and accuracy of cargo inspection methods using radiation such as X-rays are becoming more prominent.” He added, “Reflecting this trend, patent applications related to X-ray cargo inspection incorporating the latest technologies such as artificial intelligence are expected to steadily increase in the future.”

This content was produced with the assistance of AI translation services.