文献类型：期刊文献

英文题名：YOLOv10n-BC: A Novel Real-Time Object Detection Model for Driver Distracted Driving Detection

作者：Liu, Yi Li, QiaoXing Xiao, Lu Zhang, Sen

第一作者：Liu, Yi;刘毅

通信作者：Li, QX[1];Li, QX[2]

机构：[1]Guizhou Univ, Sch Management, Guiyang 550025, Peoples R China;[2]Key Lab New Power Syst Operat Control Guizhou Prov, Guiyang 550025, Peoples R China;[3]Guizhou Inst Technol, Sch Big Data, Guiyang 550025, Peoples R China;[4]Guizhou Univ, Collaborat Innovat Lab Digital Transformat & Gover, Guiyang 550025, Peoples R China;[5]GuiZhou Univ Commerce, Coll Tourism Management, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Sch Management, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Univ, Sch Management, Guiyang 550025, Peoples R China;corresponding author), Guizhou Univ, Collaborat Innovat Lab Digital Transformat & Gover, Guiyang 550025, Peoples R China.

年份：2025

卷号：E108D

期号：12

起止页码：1570-1581

外文期刊名：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

收录：;EI(收录号：20254919629905);Scopus(收录号：2-s2.0-105023311187);WOS:【SCI-EXPANDED(收录号:WOS:001629703400014)】；

基金：This work was supported by Guizhou Provincial Science and Technology Program (Qiankehe Foundation-ZK[2022] General 188 and the Project of Humanities and Social Sciences of Guizhou University (No. GDJD 202401).

语种：英文

外文关键词：distracted driving detection; YOLOv10n; multi-scale features; channel shuffle; BiFPN

摘要：and the real-time and effective detection of such behaviors can significantly reduce traffic-related injuries and fatalities. In this paper, we enhance the lightweight YOLOv10n model by integrating the BiFPN structure to bolster its multi-scale feature extraction capabilities. Additionally, we design a CASSA module that combines channel attention, spatial attention, and channel shuffle to strengthen the model's ability to capture long-range dependencies. The model was tested on the CBTDDD dataset, established in this study, which includes data on driver distraction across multiple scenarios involving sedans, passenger buses, and trucks. Compared to the original YOLOv10n model, the proposed model demonstrates a 2.0% improvement in mAP@0.5 and achieves an FPS of 115.3 f/s. These results indicate that the YOLOv10n-BC model developed in this paper is capable of performing real-time and efficient monitoring of driver distraction.