# Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans

arXiv: https://arxiv.org/abs/2004.06780 · HTML: https://mlsanity.com/paper/2004.06780/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition](https://mlsanity.com/paper/2107.07333/) (2021)
- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)
- [An Incremental Learning Approach to Automatically Recognize Pulmonary Diseases from the Multi-vendor Chest Radiographs](https://mlsanity.com/paper/2201.02574/) (2022)
- [Illicit item detection in X-ray images for security applications](https://mlsanity.com/paper/2305.01936/) (2023)
- [Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning](https://mlsanity.com/paper/2110.09319/) (2021)
- [A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items](https://mlsanity.com/paper/2201.02560/) (2022)
- [Visual inspection for illicit items in X-ray images using Deep Learning](https://mlsanity.com/paper/2310.03658/) (2023)
- [Illicit object detection in X-ray images using Vision Transformers](https://mlsanity.com/paper/2403.19043/) (2024)
- [Automated segmentation and extraction of posterior eye segment using OCT scans](https://mlsanity.com/paper/2109.10000/) (2021)
- [Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats](https://mlsanity.com/paper/2111.02651/) (2021)

## Abstract (arXiv)

In the last two decades, baggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is tedious, error-prone, and compromise privacy. Hence, many researchers have developed X-ray imagery-based autonomous systems to address these shortcomings. This paper presents a cascaded structure tensor framework that can automatically extract and recognize suspicious items in heavily occluded and cluttered baggage. The proposed framework is unique, as it intelligently extracts each object by iteratively picking contour-based transitional information from different orientations and uses only a single feed-forward convolutional neural network for the recognition. The proposed framework has been rigorously evaluated using a total of 1,067,381 X-ray scans from publicly available GDXray and SIXray datasets where it outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 on GDXray and 0.9595 on SIXray for recognizing the highly cluttered and overlapping suspicious items. Furthermore, the proposed framework computationally achieves 4.76% superior run-time performance as compared to the existing solutions based on publicly available object detectors