液晶与显示2026，Vol.41Issue(2)：240-252,13.DOI:10.37188/CJLCD.2025-0255
基于MLC-YOLO的折纸动作关键目标检测方法
MLC-YOLO based key object detection method for origami action
摘要
Abstract
To address the inter-rater and intra-rater variability,as well as the time-consuming nature of manual scoring for the"three-step command"in the Mini-Mental State Examination(MMSE),an object detection method based on MLC-YOLO is proposed to achieve automatic and precise detection of this action.First,multi-level classification object detection is performed on key human body parts and paper,and a same-class maximum confidence selection strategy is adopted to simplify post-processing and ensure the uniqueness of targets of the same class.Second,an adaptive spatial-channel decoupling module is designed to achieve efficient downsampling.Then,Ghost convolution and wavelet transform are introduced into the C3 module to enhance the efficiency and capability of multi-scale feature extraction.Finally,for the small object detection layer,spatial and channel collaborative attention is introduced to improve precision and recall in complex scenes.Experimental results indicate that the mAP95 of the proposed method reaches 61.8%,an increase of 5.6%compared to the original model,while the parameter count is reduced by 51.52%.It proves the effectiveness of the method,offering a new approach for object detection problems involving unique targets and multi-level classification,and providing an effective object detection method for the automatic scoring of the"three-step command"in MMSE.关键词
深度学习/目标检测/多级分类/轻量化网络Key words
deep learning/object detection/multilevel classification/lightweight network分类
信息技术与安全科学引用本文复制引用
陈宇聪,何宏,李泽旭,徐楚迪..基于MLC-YOLO的折纸动作关键目标检测方法[J].液晶与显示,2026,41(2):240-252,13.基金项目
国家科学技术部项目(No.G2021013008) （No.G2021013008）
中国高校产学研创新基金(No.2023RY011) （No.2023RY011）
上海理工大学医工交叉重点创新项目(No.1022308502) （No.1022308502）
华为AI算力加速计划Supported by Project of the Ministry of Science and Technology of People's Republic of China(No.G2021013008) （No.G2021013008）
China Higher Education Industry-University-Research Innovation Fund(No.2023RY011) （No.2023RY011）
Key Project of Crossing Innovation Medicine and Engineering,University of Shanghai for Science and Technology(No.1022308502) （No.1022308502）
Huawei AI Computing Power Acceleration Program （）
