{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:39:09Z","timestamp":1781534349907,"version":"3.54.5"},"reference-count":24,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2023YFF0616904"],"award-info":[{"award-number":["2023YFF0616904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science and Technology Infrastructure Platform Project","award":["APT2501-7"],"award-info":[{"award-number":["APT2501-7"]}]},{"name":"State Administration for Market Regulation","award":["AKYKF2422"],"award-info":[{"award-number":["AKYKF2422"]}]},{"award":["AKYKF2422"],"award-info":[{"award-number":["AKYKF2422"]}],"id":[{"id":"https:\/\/ror.org\/00aaqtw69","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>With the development of the Internet of Things (IoT) and microelectronics technology, the methods used to tamper with fuel dispensers have become increasingly concealed, posing significant challenges to market supervision and law enforcement. This paper releases a tampering features dataset of assembled printed circuit boards (PCBs) from fuel dispensers, aiming to provide high-quality data support for automated, computer-vision-based illicit metering detection. The dataset encompasses multi-class tampering features derived from 189 high-resolution images of PCBs seized during real-world law enforcement, covering 5 mainstream brands. To eliminate perspective bias, rigorous lens distortion correction and four-point homography transformation preprocessing were conducted on the images. Additionally, six typical tampering features (e.g., the addition of tampered surface-mount resistors) were manually and precisely annotated, and then cross-checked and confirmed by domain experts. Furthermore, the dataset was benchmarked using multiple generations of You Only Look Once (YOLO) object detection models (Baseline Validation), which have been demonstrated to handle both large and small object detection in high-resolution images. The evaluation results, including confusion matrices and t-distributed Stochastic Neighbor Embedding (t-SNE) feature clustering diagrams, demonstrate the reliability and effectiveness of this dataset for training high-precision fraud detection models. This dataset is intended to support computer vision and anti-fraud research, promoting the automated development of fuel dispenser tampering detection.<\/jats:p>","DOI":"10.3390\/data11050107","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:39:47Z","timestamp":1778150387000},"page":"107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FD-TamperBoard: A Tampering Features Dataset of Fuel Dispenser PCBs for Illicit Metering Detection"],"prefix":"10.3390","volume":"11","author":[{"given":"Chenbo","family":"Pei","sequence":"first","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-8626","authenticated-orcid":false,"given":"Xingchuang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanshuo","family":"Cao","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-1586","authenticated-orcid":false,"given":"Zilong","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"ref_1","unstructured":"(2026, March 25). Xinhua China\u2019s Annual Refining Capacity Ranks First Globally in 2023, Available online: http:\/\/english.www.gov.cn\/news\/202403\/02\/content_WS65e2c0e5c6d0868f4e8e3a0b.html."},{"key":"ref_2","unstructured":"China Petroleum Circulation Association (2024). Blue Book on the Development of China\u2019s Petroleum Circulation Industry (2023-2024), China Petroleum Circulation Association."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Heinemann, N., Bub, S., Wolfram, J., Stehle, S., Petschick, L.L., and Schulz, R. (2020). A Compendium of Chemical Class and Use Type Open Access Databases. Data, 5.","DOI":"10.3390\/data5040114"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Strauti\u0146a, S., Kalni\u0146a, I., Kaufmane, E., Sudars, K., Namat\u0113vs, I., Nikulins, A., and Edelmers, E. (2023). RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection. Data, 8.","DOI":"10.3390\/data8050086"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ramli, N., and Thio, T.H.G. (2025). Label-Efficient PCB Defect Detection with an ECA\u2013DCN-Lite\u2013BiFPN\u2013CARAFE-Enhanced YOLOv5 and Single-Stage Semi-Supervision. Sensors, 25.","DOI":"10.3390\/s25237283"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"De Oliveira, D.C., Nassu, B.T., and Wehrmeister, M.A. (2023). Image-Based Detection of Modifications in Assembled PCBs with Deep Convolutional Autoencoders. Sensors, 23.","DOI":"10.3390\/s23031353"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Houck, M.M., and Siegel, J.A. (2009). Fundamentals of Forensic Science, Academic Press.","DOI":"10.1016\/B978-0-12-374989-5.00023-5"},{"key":"ref_8","first-page":"38","article-title":"New Generation of System for the Metrological Control of Fuel Dispensers","volume":"62","author":"Kliment","year":"2021","journal-title":"OIML Bull."},{"key":"ref_9","unstructured":"Liu, J., and Chen, J. (2023). A Coarse to Fine Framework for Object Detection in High Resolution Image. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Puertas, E., De-Las-Heras, G., Fern\u00e1ndez-Andr\u00e9s, J., and S\u00e1nchez-Soriano, J. (2022). Dataset: Roundabout Aerial Images for Vehicle Detection. Data, 7.","DOI":"10.3390\/data7040047"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Adibhatla, V.A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M.F., and Shieh, J.-S. (2020). Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks. Electronics, 9.","DOI":"10.3390\/electronics9091547"},{"key":"ref_16","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, March 10). Ultralytics YOLO. GitHub Repository. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024, January 17\u201321). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_18","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, J., Kang, B., Liu, C., Peng, X., and Bai, Y. (2024). YOLO-BFRV: An Efficient Model for Detecting Printed Circuit Board Defects. Sensors, 24.","DOI":"10.3390\/s24186055"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tang, J., Liu, S., Zhao, D., Tang, L., Zou, W., and Zheng, B. (2023). PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5. Sustainability, 15.","DOI":"10.3390\/su15075963"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, S., and Deng, W. (2015, January 3\u20136). Very Deep Convolutional Neural Network Based Image Classification Using Small Training Sample Size. Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Feng, B., and Cai, J. (2023). PCB Defect Detection via Local Detail and Global Dependency Information. Sensors, 23.","DOI":"10.3390\/s23187755"},{"key":"ref_24","first-page":"2579","article-title":"Visualizing Data Using T-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. 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