{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:06:00Z","timestamp":1789391160477,"version":"build-2803163510"},"reference-count":33,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Manual industrial patrol inspection relies heavily on on-site visual observation, while the visual attention of inspectors toward specific target equipment is difficult to quantify. This study proposes an industrial patrol inspection visual attention analysis method integrating eye tracking and deep learning-based object detection. A wearable eye-tracking device is used to acquire first-person inspection videos, eye-tracking data, and camera parameters. After temporal alignment of the multimodal data, the 3D gaze points are projected onto the corresponding 2D inspection video frames. YOLO12m is employed to detect target equipment, followed by frame-by-frame matching between the 2D gaze points and the equipment bounding boxes. Based on the matching results, seven visual attention metrics are calculated to quantitatively characterize visual attention during patrol inspection. On-site experiments with 10 participants yielded 21 inspection records, with overall visual attention scores ranging from 43.16 to 89.71 and averaging 75.99. Three experts independently rated the 21 records, and the system-generated ratings agreed with the majority expert ratings for 19 records. These results demonstrate that, under the present experimental conditions, the proposed method can quantify the visual attention of inspectors toward specific target equipment during industrial patrol inspection using multiple metrics and provide interpretable results.<\/jats:p>","DOI":"10.3390\/s26185817","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:09:51Z","timestamp":1789387791000},"page":"5817","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Visual Attention Analysis Method for Industrial Patrol Inspection Based on Eye Tracking and Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Wanqi","family":"Dai","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Xuefei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Jinyi","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Xiubo","family":"Chen","sequence":"additional","affiliation":[{"name":"Rizhao Chengtou Environment and Technology Group Company Limited, Rizhao 276500, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7842-9064","authenticated-orcid":false,"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6176-3624","authenticated-orcid":false,"given":"Sheng","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","first-page":"344","article-title":"Comparative Behavioral Analysis of Expert and Novice Inspectors in Equipment Patrol Inspections","volume":"20","author":"Mitake","year":"2026","journal-title":"Int. J. Autom. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13638-021-01942-2","article-title":"Power Equipment Fault Information Acquisition System Based on Internet of Things","volume":"2021","author":"Wang","year":"2021","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1108\/JQME-01-2023-0007","article-title":"A Computer Vision Approach to Improve Maintenance Automation for Thermal Power Plants Lubrication Systems","volume":"29","author":"Bao","year":"2023","journal-title":"J. Qual. Maint. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aust, J., and Pons, D. (2022). Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades. Appl. Sci., 12.","DOI":"10.3390\/app12042250"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e12117","DOI":"10.1016\/j.heliyon.2022.e12117","article-title":"Evaluation of Expert Skills in Refinery Patrol Inspection: Visual Attention and Head Positioning Behavior","volume":"8","author":"Takamido","year":"2022","journal-title":"Heliyon"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s10100-019-00628-x","article-title":"Application of Hidden Markov Models to Eye Tracking Data Analysis of Visual Quality Inspection Operations","volume":"28","author":"Ulutas","year":"2020","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kapp, S., Barz, M., Mukhametov, S., Sonntag, D., and Kuhn, J. (2021). ARETT: Augmented Reality Eye Tracking Toolkit for Head Mounted Displays. Sensors, 21.","DOI":"10.3390\/s21062234"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, S., Deng, G., Lin, B., Jing, W., Li, Y., and Zhao, X. (2024). Real-Time Object Detection from UAV Inspection Videos by Combining YOLOv5s and DeepStream. Sensors, 24.","DOI":"10.3390\/s24123862"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, M., and Peng, J. (2026). Power Defect Detection with Improved YOLOv12 and ROI Pseudo Point Cloud Visual Analytics. Sensors, 26.","DOI":"10.3390\/s26020445"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJDST.317938","article-title":"Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation","volume":"14","author":"Jin","year":"2023","journal-title":"Int. J. Distrib. Syst. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4938","DOI":"10.1109\/JSEN.2020.3030934","article-title":"A Simple Positioning System for Large-Scale Indoor Patrol Inspection Using Foot-Mounted INS, QR Code Control Points, and Smartphone","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, J., and Qi, Y. (2022). A Multi-User Collaborative AR System for Industrial Applications. Sensors, 22.","DOI":"10.3390\/s22041319"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16404","DOI":"10.1038\/s41598-026-47229-8","article-title":"Study on the Optimization of Natural Gas Station Inspection Paths Considering Station Priority","volume":"16","author":"Peng","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_14","first-page":"1419","article-title":"Usability Analysis of Virtual-Reality-Enabled Digital Twin for the Inspection of Sewage Pumping Stations","volume":"24","author":"Hesselink","year":"2024","journal-title":"Int. J. Constr. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shao, Z., Liu, T., Li, J., and Tang, H. (2024). Virtual Inspection System for Pumping Stations with Multimodal Feedback. Sensors, 24.","DOI":"10.3390\/s24154932"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1007\/s00170-025-16750-x","article-title":"A Wearable Augmented Reality System for Intelligent Maintenance of Mechanical Equipment","volume":"141","author":"Xu","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tian, L., Zhao, H., Zhu, L., Jiang, H., and Gao, X. (2026). Edge Intelligence-Based Rail Transit Equipment Inspection System. Sensors, 26.","DOI":"10.3390\/s26010236"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Im, D., and Jeong, J. (2021). R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry. Processes, 9.","DOI":"10.3390\/pr9112043"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Oh, S.-J., Jung, M.-J., Lim, C., and Shin, S.-C. (2020). Automatic Detection of Welding Defects Using Faster R-CNN. Appl. Sci., 10.","DOI":"10.3390\/app10238629"},{"key":"ref_20","first-page":"101032","article-title":"Automatic Visual Inspection for Printed Circuit Board via Novel Mask R-CNN in Smart City Applications","volume":"44","author":"Lian","year":"2021","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89278","DOI":"10.1109\/ACCESS.2019.2925561","article-title":"Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6045","DOI":"10.1038\/s41598-025-88527-x","article-title":"A Steel Surface Defect Detection Method Based on Improved RetinaNet","volume":"15","author":"Yang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114975","DOI":"10.1016\/j.measurement.2024.114975","article-title":"Real-Time Detection of Plastic Part Surface Defects Using Deep Learning-Based Object Detection Model","volume":"235","author":"Arslankaya","year":"2024","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhou, Q., Jin, H., Li, Q., and Dai, Y. (2024). A Real-Time Intelligent Valve Monitoring Approach through Cameras Based on Computer Vision Methods. Sensors, 24.","DOI":"10.3390\/s24165337"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bao, W., Du, X., Wang, N., Yuan, M., and Yang, X. (2022). A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14205176"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107720","DOI":"10.1016\/j.chb.2023.107720","article-title":"Using Eye Tracking to Examine Expert\u2013Novice Differences during Simulated Surgical Training: A Case Study","volume":"144","author":"Li","year":"2023","journal-title":"Comput. Hum. Behav."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"16963","DOI":"10.1038\/s41598-023-43693-8","article-title":"An Eye Tracking Investigation of Attention Mechanism in Driving Behavior under Emotional Issues and Cognitive Load","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105341","DOI":"10.1016\/j.autcon.2024.105341","article-title":"Influence of Visual Environments on Struck-by Hazards for Construction Equipment Operators through Virtual Eye-Tracking","volume":"161","author":"Hong","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aust, J., Mitrovic, A., and Pons, D. (2021). Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study. Sensors, 21.","DOI":"10.3390\/s21186135"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1038\/s41598-023-29950-w","article-title":"Analysis of Gaze Patterns during Facade Inspection to Understand Inspector Sense-Making Processes","volume":"13","author":"Saleem","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_31","first-page":"158","article-title":"Integration of Eye-Tracking and Object Detection in a Deep Learning System for Quality Inspection Analysis","volume":"11","author":"Cho","year":"2024","journal-title":"J. Comput. Des. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0165-0270(03)00151-1","article-title":"Defining the Temporal Threshold for Ocular Fixation in Free-Viewing Visuocognitive Tasks","volume":"128","author":"Manor","year":"2003","journal-title":"J. Neurosci. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"04018115","DOI":"10.1061\/(ASCE)CO.1943-7862.0001589","article-title":"Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology","volume":"145","author":"Jeelani","year":"2019","journal-title":"J. Constr. Eng. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5817\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:32:57Z","timestamp":1789389177000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5817"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":33,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185817"],"URL":"https:\/\/doi.org\/10.3390\/s26185817","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}