{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:15:31Z","timestamp":1771002931558,"version":"3.50.1"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>With the acceleration of globalization and the booming development of e-commerce, the logistics distribution industry is experiencing unprecedented growth. However, this comes with higher demands for distribution efficiency and quality. How to utilize advanced technology for real-time load monitoring of logistics distribution vehicles has become an important issue in current research. Logistics delivery vehicles play a crucial role in modern supply chain management. They are not only responsible for transporting goods from suppliers to consumers, but also directly affect delivery efficiency, transportation costs, and overall customer satisfaction. However, with the rapid growth of e-commerce and customers\u2019 increasing demands for delivery speed and reliability, the logistics industry faces the dual challenges of optimizing delivery efficiency and ensuring transportation safety. This not only relates to distribution efficiency but also directly affects transportation costs and safety. Although some solutions have been proposed in existing research, there are still some problems and challenges. This paper mainly studies two methods of real-time load monitoring of logistics distribution vehicles based on image deep learning technology: one is multi-view cargo detection and localization based on Graph Location Networks (GLNs); the other is cargo recognition based on an improved DeepLabv3+ semantic segmentation model. These two methods can not only achieve accurate cargo detection and localization but also realize real-time load monitoring, providing an effective solution for the logistics distribution industry. This study proposes an image-based deep learning approach aimed at overcoming these challenges through advanced technical means, providing a comprehensive and practical solution. These methods are suitable for logistics delivery vehicles of various types and sizes, from small delivery vans to large trucks, and can adapt to different goods and loading states. More importantly, our research is designed and tested based on real-world application scenarios, ensuring the practical feasibility and effectiveness of the proposed methods. By conducting actual tests in multiple logistics centers and on delivery routes, we have verified the effectiveness of these methods in real-time monitoring of vehicle load, improving delivery efficiency, and ensuring transportation safety. Such practical foundations not only prove the feasibility of the methods but also provide valuable data and experience for further optimization and application.<\/jats:p>","DOI":"10.1177\/14727978251322026","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:19:28Z","timestamp":1741097968000},"page":"561-574","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring real-time load monitoring for logistics distribution vehicles through advanced image deep learning techniques"],"prefix":"10.1177","volume":"25","author":[{"given":"Ting","family":"Dong","sequence":"first","affiliation":[{"name":"Mapua University"},{"name":"Yulin University"}]},{"given":"Mary Jane C.","family":"Samonte","sequence":"additional","affiliation":[{"name":"Mapua University"}]}],"member":"179","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJBM.2020.105625"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/108\/5\/052007"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/su151310239"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/JCM-226529"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/24725579.2021.1968547"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/25726668.2021.1937455"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11071833"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3034385"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/itr2.12136"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2022.3168244"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/13675567.2021.1923669"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techsoc.2021.101789"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.17559\/TV-20221212144331"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rtbm.2018.03.002"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/su16031081"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20123460"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/13694332211033956"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2585513"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2893777"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20123407"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10534-z"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1002\/itl2.360"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2021.3135466"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/1729881418813778"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251322026","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978251322026","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978251322026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:30:59Z","timestamp":1771000259000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978251322026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/14727978251322026"],"URL":"https:\/\/doi.org\/10.1177\/14727978251322026","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}