{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:00:30Z","timestamp":1790240430768,"version":"4.1.0"},"reference-count":114,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"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>This study presents a lightweight RGB vision-based express-parcel detection framework for complex logistics environments. An Express Parcel Recognition (EPR) dataset is first constructed from RGB images acquired through multi-source data collection, followed by unified re-annotation and Active Learning-based pseudo-labeling to reduce manual annotation effort while maintaining label consistency. Based on YOLOv11s, a lightweight detector termed EPR-YOLO is developed by integrating SFAConv and NGAM to strengthen multi-scale feature extraction and global semantic modeling from RGB visual inputs while controlling computational complexity. Quantization-Aware Training is further introduced to obtain an INT8-quantized model with reduced storage requirements and limited accuracy degradation, providing a basis for subsequent lightweight deployment. Comparative experiments, ablation studies, and independent test set evaluations demonstrate that EPR-YOLO achieves an mAP50 of 71.654%, improving the YOLOv11s baseline by 0.854 percentage points while reducing the parameter count by 9.5%; the quantized model further retains most of the detection capability of its floating-point counterpart. In addition, a FastAPI-based Web application is implemented to support RGB image acquisition-based single-image and batch detection, result visualization, and online inference. The proposed framework therefore provides an integrated sensing-to-deployment solution covering RGB image-based parcel perception, dataset construction, lightweight model optimization, quantized deployment, and application-level validation for intelligent logistics systems.<\/jats:p>","DOI":"10.3390\/s26196036","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T14:03:26Z","timestamp":1790172206000},"page":"6036","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight RGB Vision-Based Express Parcel Detection Framework with Active Learning and Quantization-Aware Training"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4043-3003","authenticated-orcid":false,"given":"An-Qi","family":"Wu","sequence":"first","affiliation":[{"name":"Yantai Institute, China Agricultural University, Yantai 264670, China"},{"name":"Department of Crop and Soil Sciences, College of Agriculture and Environmental Sciences, University of Georgia, Tifton, GA 31793, USA"}],"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":"Fan","family":"Guo","sequence":"additional","affiliation":[{"name":"Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, 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"}]},{"given":"Weijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Yantai Institute, China Agricultural University, Yantai 264670, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9051-0094","authenticated-orcid":false,"given":"Rui-Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Crop and Soil Sciences, College of Agriculture and Environmental Sciences, University of Georgia, Tifton, GA 31793, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4362-1210","authenticated-orcid":false,"given":"Pingfan","family":"Hu","sequence":"additional","affiliation":[{"name":"Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cano, J.A., Londo\u00f1o-Pineda, A., and Rodas, C. 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