Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module
State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University（北京航空航天大学大数据精准医疗北京高精尖创新中心）
Security inspection often deals with a piece of baggage or suitcase where objects are heavily overlapped with each other, resulting in an unsatisfactory performance for prohibited items detection in X-ray images. In the literature, there have been rare studies and datasets touching this important topic. In this work, we contribute the first high-quality object detection dataset for security inspection, named Occluded Prohibited Items X-ray (OPIXray) image benchmark. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. The test set is further divided into three occlusion levels to better understand the performance of detectors. Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors. Despite the heavy occlusion in X-ray imaging, shape appearance of objects can be preserved well, and meanwhile different materials visually appear with different colors and textures. Motivated by these observations, our DOAM simultaneously leverages the different appearance information of the prohibited item to generate the attention map, which helps refine feature maps for the general detectors. We comprehensively evaluate our module on the OPIXray dataset, and demonstrate that our module can consistently improve the performance of the state-of-the-art detection methods such as SSD, FCOS, etc, and significantly outperforms several widely-used attention mechanisms. In particular, the advantages of DOAM are more significant in the scenarios with higher levels of occlusion, which demonstrates its potential application in real-world inspections. The OPIXray benchmark and our model are released at https://github.com/OPIXray-author/OPIXray.
State Key Laboratory of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；iFLYTEK Research（科大讯飞研究院）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）
Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport …
University of Chinese Academy of Sciences（中国科学院大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）；Kingsoft（金山公司）
In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance. We propose an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties. CHR assumes that each input image is …
Can a Second-View Image Be a Language? Geometric and Semantic Cross-Modal Reasoning for X-ray Prohibited Item Detection
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；State Key Laboratory of Complex & Critical Software Environment, Beihang University（北航复杂与关键软件环境国家重点实验室）
State Key Laboratory of Computer Science, ISCAS（中国科学院软件研究所计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Hangzhou Institute for Advanced Study, UCAS（中国科学院大学杭州高等研究院）；JD Finance America Corporation（京东金融美国公司）；Beihang University（北京航空航天大学）
Automatic security inspection using computer vision technology is a challenging task in real-world scenarios due to various factors, including intra-class variance, class imbalance, and occlusion. Most of the previous methods rarely solve the cases that the prohibited items are deliberately hidden in messy objects due to the lack of large-scale datasets, restricted their applications in real-world scenarios. Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especiall…
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Samsung Electro-Mechanics（三星电机）；LG AI Research（LG AI研究院）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）
Beihang University（北京航空航天大学）；Shen Yuan Honors College, Beihang University（北航沈元荣誉学院）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine（北京大数据精准医学高精尖创新中心）
Security inspection is X-ray scanning for personal belongings in suitcases, which is significantly important for the public security but highly time-consuming for human inspectors. Fortunately, deep learning has greatly promoted the development of computer vision, offering a possible way of automatic security inspection. However, items within a luggage are randomly overlapped resulting in noisy X-ray images with heavy occlusions. Thus, traditional CNN-based models trained through common image recognition datasets fail to achieve satisfactory performance in this scenario. To address these probl…
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of North Texas（北德克萨斯大学）
Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberatel…
AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School of Innovation, Northeastern University（东北大学佛山创新研究生院）
College of Information Science and Engineering, Northeastern University（信息科学与工程学院，东北大学）；State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University（过程工业综合自动化国家重点实验室，东北大学）
Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Chinese Academy of Sciences（中国科学院）；University of Science and Technology of China（中国科学技术大学）；iFLYTEK（科大讯飞）；JD Explore Academy（京东探索研究院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Adversarial attacks are valuable for evaluating the robustness of deep learning models. Existing attacks are primarily conducted on the visible light spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting texture-free X-ray images remain underexplored, despite the widespread application of X-ray imaging in safety-critical scenarios such as the X-ray detection of prohibited items. In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-crit…
Vision Transformer for Robust Occluded Person Reidentification in Complex Surveillance Scenes
China Tower Corporation Limited（中国铁塔股份有限公司）；China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区）；Beijing University of Posts and Telecommunications（北京邮电大学）
Open-Vocabulary X-ray Prohibited Item Detection via Fine-tuning CLIP
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School of Innovation, Northeastern University（东北大学佛山创新研究院）
针对 X 射线安检中新增违禁品类别识别难题，本文提出带瓶颈适配器的 OVXD 模型，通过微调 CLIP 对齐 X 射线图像与文本，并在 PIXray、PIDray 上显著提升新类别检测性能。
Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Electric Power Research Institute, Yunnan Power Grid Co., Ltd.（云南电网有限责任公司电力科学研究院）
