Automated Detection of Threat Objects Using Adapted Implicit Shape Model

Riffo, Vladimir; Mery, Domingo

doi:10.1109/tsmc.2015.2439233

Cited by 88 publications

(31 citation statements)

References 26 publications

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“…While many of these schemes utilized SURF [ 28 ], and FAST-SURF [ 29 ] (coupled with Bag of Words), some of them also fused SIFT and SPIN features in conjunction with the Support Vector Machines (SVM) for classifying baggage threats from the multiview baggage imagery [ 8 ]. Moreover, Mery et al proposed adaptive sparse representation [ 30 ] and adapted implicit shape model (AISM) [ 31 ] schemes for detecting prohibited baggage content. In another approach, they computed 3D feature points from the structure from motion to accurately recognize baggage threat from the X-ray imagery.…”

Section: Related Workmentioning

confidence: 99%

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Hassan

Shafay

Akçay

et al. 2020

Sensors

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Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

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Section: Related Workmentioning

confidence: 99%

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Hassan

Shafay

Akçay

et al. 2020

Sensors

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“…Identification of these tactile features has been studied using different approaches. Model-based and template matching methods have been used for object recognition from multiple sensory inputs, e.g., touch and vision [19], [20]. Normally, these methods directly compare an input dataset with a codebook, and without using an uncertainty measure or selection of relevant features, which reduces the accuracy, speed and reliability of the recognition task.…”

Section: Related Workmentioning

confidence: 99%

Feeling the Shape: Active Exploration Behaviors for Object Recognition With a Robotic Hand

Martinez-Hernandez

Dodd

Prescott

2018

IEEE Trans. Syst. Man Cybern, Syst.

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“…In [8] presented a multi-view branch-and-bound algorithm for multi-view object detection such as laptop, handgun, and glass bottle using standard local features in a bag of visual words (BoW) framework with linear structural Support Vector Machine (SVM). Other researchers introduced an active vision approach [9] and the Adapted Implicit Shape Model (AISM) [10] to detect threat objects in X-ray images in GDXray [11] database. Furthermore, [12] used an attention mechanism based on CNN to identify the prohibited objects in airport Xray images.…”

Section: Introductionmentioning

confidence: 99%

Object Detection in X-ray Images Using Transfer Learning with Data Augmentation

Galvez¹,

Dadios²,

Vicerra³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Object detection in X-ray images is an interesting problem in the field of machine vision. The reason is that images from an X-ray machine are usually obstructed with other objects and to itself, therefore object classification and localization is a challenging task. Furthermore, obtaining X-ray data is difficult due to an insufficient dataset available compared with photographic images from a digital camera. It is vital to easily detect objects in an X-ray image because it can be used as decision support in the detection of threat items such as improvised explosive devices (IED's) in airports, train stations, and public places. Detection of IED components accurately requires an expert and can be achieved through extensive training. Also, manual inspection is tedious, and the probability of missed detection increases due to several pieces of baggage are scanned in a short period of time. As a solution, this paper used different object detection techniques (Faster R-CNN, SSD, R-FCN) and feature extractors (ResNet, MobileNet, Inception, Inception-ResNet) based on convolutional neural networks (CNN) in a novel IEDXray dataset in the detection of IED components. The IEDXray dataset is an X-ray image of IED replicas without the explosive material. Transfer learning with data augmentation was performed due to limited X-ray data available to train the whole network from scratch. Evaluation results showed that individual detection achieved 99.08% average precision (AP) in mortar detection and 77.29% mAP in three IED components.

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Automated Detection of Threat Objects Using Adapted Implicit Shape Model

Cited by 88 publications

References 26 publications

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Feeling the Shape: Active Exploration Behaviors for Object Recognition With a Robotic Hand

Object Detection in X-ray Images Using Transfer Learning with Data Augmentation

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