{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:04:12Z","timestamp":1789398252967,"version":"build-2803163510"},"reference-count":27,"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\/"}],"funder":[{"name":"Henan Institute of Logistics and Transportation Big Data Industry Technology, the Key Research and Development Program of Henan Province","award":["252102211082"],"award-info":[{"award-number":["252102211082"]}]},{"name":"Key Scientific Research Project of institutions of Higher Education in Henan Province","award":["24B630008"],"award-info":[{"award-number":["24B630008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Manual sorting is still the mainstream scheme for component identification in down feather quality evaluation, which suffers from low detection efficiency and poor stability. To address these drawbacks, this paper proposes a fine-grained component detection algorithm based on YOLO12 for down feather quality classification. Five typical down feather components are selected as detection targets, including discolored feathers, down filaments, immature down, feathers and pure down. A dedicated down feather object detection dataset consisting of 1140 images is established accordingly. To improve the feature representation capacity of the model for tiny objects, faint boundary features and subtle distinctions between analogous categories, this work integrates the Convolutional Gated Linear Unit (CGLU) into the A2C2f module of YOLO12. While maintaining the original feature aggregation pathways and residual architecture, the conventional MLP feed-forward branch within ABlock is replaced with convolutional gated transformation. Experimental results demonstrate that the proposed A2C2f-CGLU model achieves precision of 96.50%, recall of 94.49%, mAP50 of 98.05% and mAP50-95 of 57.89% with the optimal weights on the validation set. Compared with the original YOLO12, the mAP50-95 metric is elevated by 3.04 percentage points, and the overall performance surpasses two comparative variants, A2C2f-DFFN and A2C2f-KAN. Visualizations of PR curves, confusion matrices and real test samples validate that the proposed method effectively enhances the recognition stability of tiny down feather targets and similar classes. This research provides a visual inspection foundation for subsequent component proportion calculation, quality grade discrimination and the development of intelligent detection systems.<\/jats:p>","DOI":"10.3390\/s26185826","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:15:04Z","timestamp":1789395304000},"page":"5826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO12 Down Feather Quality Classification Method Based on A2C2f-CGLU Feature Enhancement"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2805-4750","authenticated-orcid":false,"given":"Zhihui","family":"Fan","sequence":"first","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Shaowen","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Lihong","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Xihong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109768","DOI":"10.1016\/j.foodcont.2023.109768","article-title":"Applications of computer vision systems for meat safety assurance in abattoirs: A systematic review","volume":"150","author":"Sandberg","year":"2023","journal-title":"Food Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103765","DOI":"10.1016\/j.psj.2024.103765","article-title":"CarcassFormer: An end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect","volume":"103","author":"Tran","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10462-024-10956-3","article-title":"Surface defect inspection of industrial products with object detection deep networks: A systematic review","volume":"57","author":"Ma","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"35479","DOI":"10.1109\/ACCESS.2023.3266093","article-title":"Object detection using deep learning, CNNs and vision transformers: A review","volume":"11","author":"Amjoud","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mao, M., and Hong, M. (2025). YOLO object detection for real-time fabric defect inspection in the textile industry: A review of YOLOv1 to YOLOv11. Sensors, 25.","DOI":"10.3390\/s25072270"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42816","DOI":"10.1109\/ACCESS.2024.3378568","article-title":"YOLOv1 to v8: Unveiling each variant-A comprehensive review of YOLO","volume":"12","author":"Hussain","year":"2024","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s10462-025-11253-3","article-title":"YOLO advances to its genesis: A decadal and comprehensive review of the You Only Look Once (YOLO) series","volume":"58","author":"Sapkota","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111209","DOI":"10.1016\/j.patcog.2024.111209","article-title":"BiFPN-YOLO: One-stage object detection integrating bi-directional feature pyramid networks","volume":"160","author":"Doherty","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1049\/ipr2.13073","article-title":"NHD-YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation","volume":"18","author":"Chen","year":"2024","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, B., Wang, H., Cao, Z., Wang, Y., Tao, L., Yang, J., and Zhang, K. (2024). PRC-Light YOLO: An efficient lightweight model for fabric defect detection. Appl. Sci., 14.","DOI":"10.3390\/app14020938"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ma, S., Liu, Y., and Zhang, Y. (2024). Fabric defect detection based on improved lightweight YOLOv8n. Appl. Sci., 14.","DOI":"10.3390\/app14178000"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26877","DOI":"10.1109\/JSEN.2024.3418618","article-title":"Automated defect detection for mass-produced electronic components based on YOLO object detection models","volume":"24","author":"Mao","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32830","DOI":"10.1109\/ACCESS.2024.3369705","article-title":"Experimental study on YOLO-based leather surface defect detection","volume":"12","author":"Chen","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"13467","article-title":"Towards large-scale small object detection: Survey and benchmarks","volume":"45","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, L., Ma, B., Dong, Y., Yin, Z., and Lu, F. (2025). DCFE-YOLO: A novel fabric defect detection method. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0314525"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"41988","DOI":"10.1109\/ACCESS.2024.3378739","article-title":"Hyperspectral imaging based nonwoven fabric defect detection method using LL-YOLOv5","volume":"12","author":"Lv","year":"2024","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Min, L., Fan, Z., Lv, Q., Reda, M., Shen, L., and Wang, B. (2023). YOLO-DCTI: Small object detection in remote sensing base on contextual transformer enhancement. Remote Sens., 15.","DOI":"10.3390\/rs15163970"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29294","DOI":"10.1109\/ACCESS.2024.3368848","article-title":"LAYN: Lightweight multi-scale attention YOLOv8 network for small object detection","volume":"12","author":"Ma","year":"2024","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"25624","DOI":"10.1038\/s41598-024-77513-4","article-title":"SOD-YOLO: A lightweight small object detection framework","volume":"14","author":"Xiao","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shi, D. (2024, January 16\u201322). TransNeXt: Robust Foveal Visual Perception for Vision Transformers. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01683"},{"key":"ref_22","unstructured":"Dai, T., Wang, J., Guo, H., Li, J., Wang, J., and Zhu, Z. (2024, January 3\u20139). FreqFormer: Frequency-aware Transformer for Lightweight Image Super-resolution. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, Republic of Korea."},{"key":"ref_23","unstructured":"Yang, X., and Wang, X. (2025, January 24\u201328). Kolmogorov-Arnold Transformer. Proceedings of the International Conference on Learning Representations 2025 (ICLR 2025), Singapore."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109347","DOI":"10.1016\/j.patcog.2023.109347","article-title":"A comprehensive survey of image augmentation techniques for deep learning","volume":"137","author":"Xu","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Suto, J. (2024). Using data augmentation to improve the generalization capability of an object detector on remote-sensed insect trap images. Sensors, 24.","DOI":"10.3390\/s24144502"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7193","DOI":"10.3934\/mbe.2023311","article-title":"Dynamic Mosaic algorithm for data augmentation","volume":"20","author":"Li","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TPAMI.2023.3330862","article-title":"Differentiable image data augmentation and its applications: A survey","volume":"46","author":"Shi","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:15:48Z","timestamp":1789395348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":27,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185826"],"URL":"https:\/\/doi.org\/10.3390\/s26185826","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}