Anchor-Free Weapon Detection for X-Ray Baggage Security Images
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 anchor-based object detection, this paper introduces an anchor-free mode convolutional neural network object detection...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9885033/ |
| _version_ | 1828415281383342080 |
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| author | Yan Huang Xinsha Fu Yanjie Zeng |
| author_facet | Yan Huang Xinsha Fu Yanjie Zeng |
| author_sort | Yan Huang |
| collection | DOAJ |
| description | 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 anchor-based 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 anchor-based 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. |
| first_indexed | 2024-12-10T13:42:01Z |
| format | Article |
| id | doaj.art-14bf5a1210704a54a52652a689ea9ca6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T13:42:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-14bf5a1210704a54a52652a689ea9ca62022-12-22T01:46:37ZengIEEEIEEE Access2169-35362022-01-0110978439785510.1109/ACCESS.2022.32055939885033Anchor-Free Weapon Detection for X-Ray Baggage Security ImagesYan Huang0Xinsha Fu1Yanjie Zeng2School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou, ChinaGuangdong Provincial Transport Planning and Research Center (GPTPRC), Guangzhou, ChinaConsidering 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 anchor-based 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 anchor-based 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.https://ieeexplore.ieee.org/document/9885033/Object detectionX-ray baggage security imagesanchor-free |
| spellingShingle | Yan Huang Xinsha Fu Yanjie Zeng Anchor-Free Weapon Detection for X-Ray Baggage Security Images IEEE Access Object detection X-ray baggage security images anchor-free |
| title | Anchor-Free Weapon Detection for X-Ray Baggage Security Images |
| title_full | Anchor-Free Weapon Detection for X-Ray Baggage Security Images |
| title_fullStr | Anchor-Free Weapon Detection for X-Ray Baggage Security Images |
| title_full_unstemmed | Anchor-Free Weapon Detection for X-Ray Baggage Security Images |
| title_short | Anchor-Free Weapon Detection for X-Ray Baggage Security Images |
| title_sort | anchor free weapon detection for x ray baggage security images |
| topic | Object detection X-ray baggage security images anchor-free |
| url | https://ieeexplore.ieee.org/document/9885033/ |
| work_keys_str_mv | AT yanhuang anchorfreeweapondetectionforxraybaggagesecurityimages AT xinshafu anchorfreeweapondetectionforxraybaggagesecurityimages AT yanjiezeng anchorfreeweapondetectionforxraybaggagesecurityimages |