计算机工程与应用2025，Vol.61Issue(21)：94-104,11.DOI:10.3778/j.issn.1002-8331.2504-0085
YOLO-Vega:一种面向复杂环境的轻量级番茄检测模型
YOLO-Vega:Lightweight Tomato-Detection Model for Complex Environments
摘要
Abstract
Tomato-fruit detection,a key technology for intelligent harvesting,encounters significant challenges in com-plex field conditions characterized by background clutter,foliage occlusion,fruit overlap and the resulting scale varia-tions.To address these difficulties,this paper proposes YOLO-Vega,a lightweight object-detection model built on the YOLO11n architecture.YOLO-Vega integrates an adaptive feature fusion network(AFFNet)that uses a multi-channel dynamic-weight mechanism to adaptively fuse multi-scale features derived from both the fruit itself and its surroundings;a mixed-space edge enhancement module(MSEE-C3k2)that reinforces blurred edges caused by occlusion and overlap through a combination of multi-scale context awareness and explicit high-frequency edge injection;and a key-weight detection module(KW-Detect)that improves feature selection under background interference and enhances recognition of occluded targets by generating and applying task-specific key-information weights.Experiments on benchmark datasets show that YOLO-Vega achieves an mAP@0.5 of 88.62%while maintaining a low computational overhead(2.65×106 parameters,7.3 GFLOPs)and a compact model size(6.0 MB),outperforming mainstream models in these complex scenar-ios and offering an excellent balance between accuracy and efficiency.关键词
番茄检测/轻量级目标检测/YOLO11n/自适应特征融合/边缘增强Key words
tomato detection/lightweight object detection/YOLO11n/adaptive feature fusion/edge enhancement分类
计算机与自动化引用本文复制引用
刘凯越,吴建军,李智慧,王松..YOLO-Vega:一种面向复杂环境的轻量级番茄检测模型[J].计算机工程与应用,2025,61(21):94-104,11.基金项目
国家重点研发计划(2022YFD2100202,2018YFD0401404). （2022YFD2100202,2018YFD0401404）
