SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

Miao, Caijing; Xie, Lingxi; Wan, Fang; Su, Chi; Liu, Hongye; Jiao, J. B.; Ye, Qixiang

doi:10.48550/arxiv.1901.00303

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…X-ray and thermal security images, on the other hand, lack the fine detail found in nature photography and have a restricted color spectrum, as well as low contrast and roughness. The presence of object overlap in these pictures distinguishes them, posing a challenge to deep learning models since object overlap increases intra-class differences as discussed in [2]. Because each pixel represents a muted radiation intensity that may be viewed in an image, pixels in security images convey information about object overlap.…”

Section: Introductionmentioning

confidence: 99%

ThreatNet: advanced threat detection, region-based convolutional neural network framework

Singh

Kumar

Kadry³

2022

IJEECS

View full text Add to dashboard Cite

It is critical for many countries to ensure public safety in detecting and identifying threats in a night, <span lang="EN-US">commercial places, border areas and public places. Majority of past research in this area has focused on the use of image-level categorization and object-level detection techniques. As an X-ray and thermal security image analysis strategy, object separation can considerably improve automatic threat detection when used in conjunction with other techniques. In order to detect possible threats, the effects of introducing segmentation deep learning models into the threat detection pipeline of a large imbalanced X-ray and thermal dataset were investigated. With the purpose of boosting the number of true positives discovered, a faster regional convolutional neural network (R-CNN) model was trained on a balanced dataset to identify probable hazard zones in X-ray and thermal security pictures. In order to get the final results, we combined the two models i.e faster R-CNN with Mask RCNN into a single detection pipeline using the transfer learning technique, which outperforms baseline and end-to-end instance segmentation methods using less number of the practical dataset, with mAPs ranging from 94.88 percent to 91.40 percent helps in detecting the person with guns, knives, pliers to avoid cross border threats.</span>

show abstract

Section: Introductionmentioning

confidence: 99%

ThreatNet: advanced threat detection, region-based convolutional neural network framework

Singh

Kumar

Kadry³

2022

IJEECS

View full text Add to dashboard Cite

show abstract

Automated detection of inorganic powders in X-ray images of airport luggage

2023

View full text Add to dashboard Cite

At the checkpoint, the detection of illicit inorganic powders in passenger luggage using conventional X-ray can be challenging. An algorithm is presented for the automated detection of inorganic powder-like substances from complex X-ray images of highly cluttered passenger bags using computer vision. The proposed method utilizes support vector machine (SVM) classifiers built from local binary patterns (LBP) texture features. When tested on a dataset created in-house, the algorithm achieves a detection precision of 97% and a false positive rate of 3%. This is the first study performed on a realistic dataset, including different amounts and shapes of powders and electronic clutter, and where the success of the automated method is compared with inter-observer variability.

show abstract

An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

Sara,

Mandava

2024

Nondestructive Testing and Evaluation

View full text Add to dashboard Cite

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

Cited by 3 publications

References 33 publications

ThreatNet: advanced threat detection, region-based convolutional neural network framework

ThreatNet: advanced threat detection, region-based convolutional neural network framework

Automated detection of inorganic powders in X-ray images of airport luggage

An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

Contact Info

Product

Resources

About