{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:52Z","timestamp":1777704952210,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Unmanned sorting technology can significantly improve the transportation efficiency of the logistics industry, and package detection technology is an important component of unmanned sorting. This paper proposes a lightweight deep learning network called EPYOLO, in which a lightweight self-attention feature extraction backbone network named EPnet is also designed. It also reduces the Floating-Point Operations (FLOPs) and parameter count during the feature extraction process through an improved Contextual Transformer-slim (CoTs) self-attention module and GSNConv module. To balance network performance and obtain semantic information for express packages of different sizes and shapes, a multi-scale pyramid structure is adopted using the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Finally, comparative experiments were conducted with the state-of-the-art (SOTA) model by using a self-built dataset of express packages by using a self-built dataset of express packages, results demonstrate that the mean Average Precision (mAP) of the EPYOLO network reaches 98.8%, with parameter quantity only 11.63% of YOLOv8\u200as and FLOPs only 9.16% of YOLOv8\u200as. Moreover, compared to the YOLOv8\u200as network, the EPYOLO network shows superior detection performance for small targets and overlapping express packages.<\/jats:p>","DOI":"10.3233\/jifs-232874","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T06:58:31Z","timestamp":1701845911000},"page":"12013-12025","source":"Crossref","is-referenced-by-count":1,"title":["A global lightweight deep learning model for express package detection"],"prefix":"10.1177","volume":"45","author":[{"given":"Guowei","family":"Zhang","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian province, China"}]},{"given":"Yutong","family":"Tang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian province, China"}]},{"given":"Hulin","family":"Tang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian province, China"}]},{"given":"Wuzhi","family":"Li","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian province, China"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"Research and Development Department, Shunfeng Technology Co., Ltd., Shenzhen, Guangdong Province, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-232874_ref1","doi-asserted-by":"crossref","first-page":"360","DOI":"10.3390\/su14010360","article-title":"A literature review of drone-based package delivery logistics systems and their implementation feasibility","volume":"14","author":"Taha Benarbia","year":"2021","journal-title":"Sustainability"},{"issue":"2","key":"10.3233\/JIFS-232874_ref2","doi-asserted-by":"crossref","first-page":"298","DOI":"10.3390\/e25020298","article-title":"Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting","volume":"25","author":"Chuanxiang Ren","year":"2023","journal-title":"Entropy"},{"key":"10.3233\/JIFS-232874_ref3","doi-asserted-by":"crossref","first-page":"119420","DOI":"10.1016\/j.eswa.2022.119420","article-title":"SARWAS: Deep ensemble learning techniques for sentiment based recommendation system,","volume":"216","author":"Chaitali Choudhary","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"10.3233\/JIFS-232874_ref4","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1007\/s11831-022-09765-0","article-title":"Review of ML and AutoML solutions to forecast time-series data","volume":"29","author":"Ahmad Alsharef","year":"2022","journal-title":"Archives of Computational Methods in Engineering"},{"issue":"3","key":"10.3233\/JIFS-232874_ref8","first-page":"5","article-title":"Aggregating nested transformers","volume":"2","author":"Zizhao Zhang","year":"2021","journal-title":"arXiv preprint arXiv:2105.12723"},{"issue":"14","key":"10.3233\/JIFS-232874_ref12","doi-asserted-by":"crossref","first-page":"7255","DOI":"10.3390\/app12147255","article-title":"Improved yolov5: Efficient object detection using drone images under various conditions","volume":"12","author":"Hyun-Ki Jung","year":"2022","journal-title":"Applied Sciences"},{"issue":"8","key":"10.3233\/JIFS-232874_ref18","first-page":"2472","article-title":"Improved traffic sign recognition algorithm based on YOLO v3 algorithm","volume":"40","author":"Jinhong Jiang","year":"2020","journal-title":"Journal of Computer Applications"},{"issue":"4","key":"10.3233\/JIFS-232874_ref21","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1108\/SASBE-07-2019-0083","article-title":"A robust method to authenticate car license plates using segmentation and ROI based approach","volume":"9","author":"Akarsh Aggarwal","year":"2020","journal-title":"Smart and Sustainable Built Environment"},{"issue":"3","key":"10.3233\/JIFS-232874_ref23","first-page":"033033","article-title":"YOLOv5-R: lightweight real-time detection based on improved YOLOv5","volume":"31","author":"Jian Ren","journal-title":"Journal of Electronic Imaging"},{"issue":"2","key":"10.3233\/JIFS-232874_ref31","first-page":"1489","article-title":"Contextual transformer networks for visual recognition","volume":"45","author":"Yehao Li","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/JIFS-232874_ref34","doi-asserted-by":"crossref","unstructured":"Zhaohui Zheng , et al. Distance-IoU loss: Faster and better learning for bounding box regression, Proceedings of the AAAI Conference on Artificial Intelligence 34(07) (2020).","DOI":"10.1609\/aaai.v34i07.6999"},{"issue":"30","key":"10.3233\/JIFS-232874_ref35","doi-asserted-by":"crossref","first-page":"46018","DOI":"10.1007\/s11356-022-19014-3","article-title":"Smart IoT and machine learning-based framework for water quality assessment and device component monitoring, (30)","volume":"29","author":"Akashdeep Bhardwaj","year":"2022","journal-title":"Environmental Science and Pollution Research"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232874","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:30Z","timestamp":1777455750000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232874"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":12,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232874","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}