计算机工程与应用2024，Vol.60Issue(14)：197-208,12.DOI:10.3778/j.issn.1002-8331.2403-0087
改进YOLOv8的汽车表面伤损实例分割模型
Enhancing YOLOv8 for Improved Instance Segmentation of Automotive Surface Damage
摘要
Abstract
To address the shortcomings of manual damage assessment and issues with conventional vehicle damage detection models in the context of intelligent vehicles,it proposes EIS-YOLO,an enhanced instance segmentation model based on YOLOv8.It introduces CRDB,a novel multi-scale feature fusion and channel reduction module that replaces C2f,reducing parameters by 20.15％while improving fusion efficiency.Additionally,HRFPN structure maintains high-resolution branches,facilitates finer detail and semantic exchange,and includes AFF and BiAM attention modules for deeper feature integration.An efficient E-FPN and an extra output head are utilized to better identify small damages and edges.Evaluated on CarDD dataset,CRDB improves multi-task accuracy by 2 percentage points,and the integrated EIS-YOLO model with HRFPN sees a 4.4 percentage points boost in PB and 6.6 percentage points in PM over the baseline,all while maintaining a lighter weight and lower computational complexity.关键词
汽车伤损检测/YOLO-Seg/注意力机制/多尺度特征融合/CarDD汽车伤损数据Key words
vehicle damage detection/YOLO-Seg/attention mechanism/multi-scale feature fusion/CarDD vehicle damage data分类
信息技术与安全科学引用本文复制引用
谭旭,赵骥..改进YOLOv8的汽车表面伤损实例分割模型[J].计算机工程与应用,2024,60(14):197-208,12.基金项目
辽宁自然科学基金(2020-MS-281) （2020-MS-281）
辽宁省教育厅科研项目(LJKZZ2022043). （LJKZZ2022043）
