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Found 9 papers, 1 papers with code
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts.
Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment.
Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery
When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0. 823 with Cascade R-CNN across the FLIR dataset, outperforming prior work.
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond.
On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks
Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb}) X-ray imagery.
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items.
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles.
The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagery
This work opens up the possibility of using synthetically composed imagery, avoiding the need to collate such large volumes of hand-annotated real-world imagery.
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign).
