目的 针对铝制品表面反光特性导致的检测难点及现有缺陷检测算法精度与效率不足的问题,提出一种基于YOLOv11n模型的高精度铝制品表面缺陷检测算法MEP-Net（Multi-scale enhanced perception network）。方法 首先,该算法提出一种具有多尺度感知和边缘信息增强能力的新型卷积MSEConv（Multi-scale enhanced convolution）,将其嵌入C3K2结构中构建C3K2-MSE模块,通过跨层多尺度特征交互,有效提升模型对多尺度目标和细节特征的捕捉能力;其次,设计结合局部与全局注意力分支（Local_Global Attention）的层次注意力融合模块HAFB（Hierarchical attention fusion block）,通过层次化的特征处理路径对输入特征进行精细化融合,在保留空间细节特征的同时,利用自适应加权机制增强全局上下文的语义表达能力;然后,利用DWConv和共享参数卷积设计新型检测头P2S-Detect（Packet sharing parameter detect）,在确保精度不变的情况,实现模型轻量化;最后,采用MPDIoU损失函数替换CIoU损失函数,加快模型收敛速度,提升目标定位能力。结果 在铝制金属盖缺陷数据集和NEU-DET公开数据集上,MEP-Net的mAP@0.5分别达到97.5%和78.4%,相较原始YOLOv11n基准模型分别提升了2.9%、1.6%。同时在铝质金属盖缺陷数据集上,召回率R提升了1.7%,检测速度达142.6帧/s。结论 MEP-Net在铝质金属盖缺陷检测上兼备高精度与实时性,为金属瓶盖封装质检提供了一种高效可行的技术方案。
Abstract
To address the detection challenges caused by the surface reflection characteristics of aluminum products and the insufficient accuracy and efficiency of existing defect detection algorithms, the work aims to propose a YOLOv11n model based high-precision surface defect detection algorithm for aluminum products, MEP-Net (Multi-scale Enhanced Perception Network). Firstly, the algorithm proposed a novel multi-scale enhanced convolution (MSEConv) with multi-scale perception and edge information enhancement capabilities. This convolution was embedded into the C3K2 structure to construct the C3K2-MSE module. Through cross-layer multi-scale feature interaction, it effectively improved the model's ability to capture multi-scale targets and detailed features. Secondly, a Hierarchical Attention Fusion Block (HAFB) was designed, integrating local and global attention branches (Local_GlobalAttention). This module performed fine-grained feature fusion through hierarchical processing paths, preserving spatial detail features while enhancing the semantic expression of global context via an adaptive weighting mechanism. Then, a new detection head called P2S-Detect (Packet sharing Parameter Detect) was developed with DWConv and shared parameter convolution. This design achieved model lightweighting without compromising accuracy. Finally, the MPDIoU loss function replaced the CIoU loss function, accelerating model convergence and improving target localization accuracy. On the aluminum metal cap defect dataset and the public NEU-DET dataset, MEP-Net achieved mAP@0.5 values of 97.5% and 78.4% respectively, representing improvements of 2.9% and 1.6% compared to the original YOLOv11n baseline model. Meanwhile, on the aluminum metal cap defect dataset, the recall rate R increased by 1.7%, and the detection speed reached 142.6 FPS. MEP-Net demonstrates both high accuracy and real-time performance in aluminum metal cap defect detection, providing an efficient and feasible technical solution for quality inspection in metal bottle cap packaging.
关键词
YOLOv11n /
MSEConv卷积 /
C3K2-MSE /
层次注意力融合模块 /
P2S-Detect
Key words
YOLOv11n /
MSEConv convolution /
C3K2-MSE /
hierarchical attention fusion module /
P2S-Detect
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基金
烟台市2023年校地融合发展项目（2323013-2023XDRH001）;山东省科技型中小企业创新能力提升工程计划项目（2023TSGC0823）;烟台市科技型中小企业创新能力提升工程计划项目（2023TSGC112）
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