# On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery

arXiv: https://arxiv.org/abs/1911.08216 · HTML: https://mlsanity.com/paper/1911.08216/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)
- [PANDA : Perceptually Aware Neural Detection of Anomalies](https://mlsanity.com/paper/2104.13702/) (2021)
- [Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption](https://mlsanity.com/paper/2303.03925/) (2023)
- [Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery](https://mlsanity.com/paper/2404.12285/) (2024)
- [Anomaly Detection with Ensemble of Encoder and Decoder](https://mlsanity.com/paper/2303.06431/) (2023)

## Abstract (arXiv)

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within complex electronic items. Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. Relative performance is evaluated over an extensive dataset of exemplar cluttered X-ray imagery, with a focus on consumer electronics items. We find that sub-component level segmentation produces marginally superior performance in the secondary anomaly detection via classification stage, with true positive of ~98% of anomalies, with a ~3% false positive.