Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images

Chang, An; Zhang, Yunlong; Zhang, Shunli; Zhong, Leisheng; Zhang, Li

doi:10.1016/j.knosys.2021.107916

Cited by 32 publications

(13 citation statements)

References 21 publications

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“…Based on SIXray dataset, many researchers proposed various prohibited object detection methods according to the characteristics of X-ray images, and the detection performance has been continuously improved. In order to solve the problem of sample imbalance in X-ray prohibited object detection, Chen et al [36] proposed a hard-negative-sample selection scheme, and introduced it into Faster R-CNN [16].…”

Section: X-ray Prohibited Object Detectionmentioning

confidence: 99%

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Zhang

et al. 2023

Preprint

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Fast and accurate prohibited object detection in X-ray images is great challenging. Based on YOLOv6 object detection framework, in this paper, Channel-Target Attention Feature Pyramid Network (CTA-FPN) is proposed for prohibited object detection in X-ray images. It includes two key components: TAAM (Target Aware Attention Module) and CAM (Channel Attention Module). TAAM is to generate the target attention map to enhance the features of prohibited object regions and suppress those of the background regions, so as to solve the problems of object occlusion and cluttered background in X-ray images. CAM is to highlight the feature channels important to the detection tasks, and suppress the irrelevant ones. The target-wise and channel-wise feature enhancement can effectively strengthen the feature representation capability of the network. The proposed CTA-FPN is incorporated into S, M and L models of YOLOv6 respectively, obtaining three X-ray prohibited object detection models. The experimental results on two publicly available benchmark datasets of SIXray and CLCXray show that, CTA-FPN can effectively improve the detection performance of YOLOv6. Especially, YOLOv6-CTA-FPN-L can achieve the state-of-the-arts detection accuracy.

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Section: X-ray Prohibited Object Detectionmentioning

confidence: 99%

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Zhang

et al. 2023

Preprint

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“…X-ray security inspection machines show different colors for different material items by the object distinction in absorption X-ray [22]. Therefore, it has many applications in many tasks, such as security inspection [4,[25][26][27]and medical imaging analysis [8,[28][29][30][31][32][33]. However, there are very few X-ray image datasets due to the particularity of security inspection scenes.…”

Section: Security Inspection Image Datasetmentioning

confidence: 99%

“…Different from traditional detection tasks, in this scenario, there are various items in the passenger's luggage and random permutations between items, resulting in heavily cluttered X-ray images [1][2][3][4]. Therefore, object detection algorithms for general natural images do not perform well on cluttered X-ray images as in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Obtained edge images by using the traditional edge detection algorithm Sobel. Chang et al [4] found that different classes of prohibited objects have a clear distinction in physical size and used Otsu's threshold segmentation algorithm [21] to segment the original image into foreground and background, treating the foreground region as the approximate size of the detected object. Although these two methods improve the detection accuracy to a certain extent by introducing such prior information, the obtained prior information is easily disturbed by other irrelevant information due…”

Section: Introductionmentioning

confidence: 99%

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Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

et al. 2023

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Prohibited item detection in X-ray images is an effective measure to maintain public safety. Recent prohibited item detection methods based on deep learning has achieved impressive performance. Some methods improve prohibited item detection performance by introducing prior knowledge of prohibited items, such as the edge and size of an object. However, items within baggage are often placed randomly, resulting in cluttered X-ray images, which can seriously affect the correctness and effectiveness of prior knowledge. In particular, we find that different material items in X-ray images have clear distinctions according to their atomic number Z information, which is vital to suppress the interference of irrelevant background information by mining material cues. Inspired by this observation, in this paper, we combined the atomic number Z feature and proposed a novel atomic number Z Prior Guided Network (ZPGNet) to detect prohibited objects from heavily cluttered X-ray images. Specifically, we propose a Material Activation (MA) module that cross-scale flows the atomic number Z information through the network to mine material clues and reduce irrelevant information interference in detecting prohibited items. However, collecting atomic number images requires much labor, increasing costs. Therefore, we propose a method to automatically generate atomic number Z images by exploring the color information of X-ray images, which significantly reduces the manual acquisition cost. Extensive experiments demonstrate that our method can accurately and robustly detect prohibited items from heavily cluttered X-ray images. Furthermore, we extensively evaluate our method on HiXray and OPIXray, and the best result is 2.1% mAP50 higher than the state-of-the-art models on HiXray.

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“…The experiments showed that YOLOv3 [7] obtained the best mAP, with 96.3% for guns, 76.2% for knives, 86.9% for razor blades and 93.7% for shuriken, while the average mAP for all threat objects was 80.0%. Chang, An, et al [31] proposed a two-stage prohibited object detection network that can identify prohibited objects in heavily cluttered X-ray baggage images to reduce the false positives caused by neglecting the actual physical sizes of items. Extensive experimentation demonstrated that the proposed method outperformed stateof-the-art object detection methods.…”

Section: Related Workmentioning

confidence: 99%

Anchor-Free Weapon Detection for X-Ray Baggage Security Images

Huang

Zeng³

2022

IEEE Access

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Considering the real-time and high-precision requirements of image processing in X-ray baggage security screening; and problems such as the inflexibility and complex computation of anchorbased object detection, this paper introduces an anchor-free mode convolutional neural network object detection method for detecting weapons (knives and handguns) in X-ray baggage security images. The advantage of the anchor-free method over the anchor-based method is that the size of the anchor box does not have to be set, and the generalization ability is strong; the absence of the anchor box reduces the number of computations, and solves the problem of unbalanced positive and negative samples in the anchor-based method. To fully evaluate the effectiveness of the anchor-free method for X-ray baggage screening image detection, a large number of images containing knives and handguns were collected and annotated in the early stages of this work to produce a dataset that could be used for training. Six mainstream anchor-free methods (CornerNet, CenterNet, CornerNet-Lite, ExtremeNet, Objects as Points and You Only Look Once(YOLOx)) are introduced. For experimental integrity, this paper adds an anchorbased comparison experiment, using Faster-RCNN, YOLOv3 and YOLOv5 to perform the same work. The experimental results show that the YOLOx, Objects as Points and ExtremeNet anchor-free methods used in this paper have excellent performance in weapon detection in X-ray baggage security images. Among them, the mean average precision (mAP) of YOLOx combined with the CSPDarknet53 network reached 0.905, and the mAP of ExtremeNet combined with the Hourglass-104 network reached 0.900; the performance of the Objects as Points method was also good. All these methods performed better than the anchor-based methods compared in this paper. Therefore, we believe that the anchor-free method has a practical effect in weapon detection for X-ray luggage images.INDEX TERMS Object detection, X-ray baggage security images, anchor-free

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Detecting prohibited objects with physical size constraint from cluttered X-ray baggage images

Cited by 32 publications

References 21 publications

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

Anchor-Free Weapon Detection for X-Ray Baggage Security Images

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