A method for automated detection of illegal substances smuggled inside internal cavities of a passenger, e.g., as capsules. The method provides for an automated detection of narcotics hidden in a passenger's stomach area using pictures produced by an X-ray scanner. According to an exemplary embodiment, throughput of the scanner is increased by an automated detection algorithm, which takes less time than visual analysis by an operator. The operator is only involved in cases when narcotics are detected. The automated detection method has a consistent precision, because the effects of tiredness of the operator are eliminated. Efficiency and costs of the process are improved, since fewer qualified operators can service several scanners.

An image generator built by machine learning is stored in an image-generator storage section (31). The image generator receives an image having a missing region and fills that region with a complementary image. An inspection target image is stored in an image storage section (32). A missing-image generator (44) generates, from the inspection target image, a missing image (63) in which a region is missing, a window (62) having a specified shape and size being applied on the region. A complemented-image generator (45) generates a complemented image (65) having the region filled with a complementary image by inputting the missing image into the image generator. A difference acquirer (46) determines a difference (66) between the inspection target image and the complemented image. A determiner (47) determines whether the region is normal or abnormal by comparing the difference with a predetermined criterion.

A system for automated inspection of an installed fastener includes a radiographic generator, a radiographic sensor, and a computer device. The radiographic generator is operable for directing scanning beams of electromagnetic radiation, e.g., x-rays, toward a workpiece having the installed fastener, for instance a rivet. The radiographic sensor collects a radiographic image of a workpiece inclusive of the installed fastener. The computer device processes the image via one or more trained characterization models to localize the installed fastener within the image, and identifies a presence or absence of a defect in the installed fastener. The computer device also characterizes a quality of the installed fastener based on the presence or absence of the defect, and thereafter generates an inspection report indicative of the quality of the fastener. This may include generating an annotated or color-coded image of the installed fastener.