The inspection of visual quality represents a crucial step in the production process of bottled products. Numerous machine vision methodologies have demonstrated proficient identification of significant defects on bottle surfaces in well-controlled imaging environments. However, the actual production scenario introduces a myriad of surface defect types on bottled products, exhibiting diverse shapes, with the majority of defect instances characterized by relatively small individual areas. Faced with such diverse and small-sized defects, traditional convolutional neural networks (CNNs) encounter limitations due to the utilization of fixed-size convolutional kernels. This choice results in a constrained receptive field, hindering the capture of sufficiently effective contextual information. Additionally, pooling operations within traditional CNNs lead to a substantial reduction in feature map dimensions, causing excessive blurring or outright neglect of small-sized defects. Consequently, this detrimentally impacts the accuracy of surface defect detection and recognition. This study proposes a novel multi-scale defect detection model incorporating a variable receptive field and Gather–Distribute feature fusion mechanism to overcome limitations of traditional CNNs. Nine different scale defect images of bottled products, including worn, wrinkles, joint markings and etc, were collected and enhanced to construct a surface defect detection dataset for the actual production of bottled products. Enhancements to convolutional layers and the C2f module improve feature extraction for different defect shapes and sizes. Integration of the Gather–Distribute feature fusion module (GDFFM) reduces feature information loss and enhances shallow-layer feature utilization. An efficient detection head (E-Detect) with ”parameter sharing” is proposed to reduce computational complexity and improve detection speed without experiencing significant accuracy loss. Experimental results demonstrate that the model’s superiority over advanced defect detection algorithms across various categories. Notably, it achieves accuracies of 89.9%, 55.6%, 89.4%, and 75% for challenging defects like small worn, subtle wrinkles, inclined external cover, and misaligned labels, with improvements of 3.2%, 7%, 2%, and 11.8% over the baseline model, respectively. The model’s mean Average Precision (mAP) also increases by 2.7%.

中文翻译：

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基于可变感受野和Gather-Distribute特征融合机制的瓶装产品多尺度表面缺陷检测方法

视觉质量检验是瓶装产品生产过程中的关键步骤。许多机器视觉方法已证明可以在良好控制的成像环境中熟练识别瓶子表面的重大缺陷。然而，实际生产场景在瓶装产品上引入了多种表面缺陷类型，呈现出不同的形状，并且大多数缺陷实例的特征是相对较小的单个区域。面对如此多样化和小尺寸的缺陷，传统的卷积神经网络（CNN）由于使用固定大小的卷积核而遇到限制。这种选择会导致接受域受限，阻碍捕获足够有效的上下文信息。此外，传统 CNN 中的池化操作会导致特征图尺寸大幅减小，从而导致过度模糊或完全忽略小尺寸缺陷。因此，这不利地影响表面缺陷检测和识别的准确性。本研究提出了一种新颖的多尺度缺陷检测模型，结合了可变感受野和收集-分布特征融合机制，以克服传统 CNN 的局限性。收集并增强瓶装产品九种不同尺度的缺陷图像，包括磨损、皱纹、接缝标记等，以构建瓶装产品实际生产的表面缺陷检测数据集。卷积层和 C2f 模块的增强改进了不同缺陷形状和尺寸的特征提取。集成收集-分布特征融合模块（GDFFM）减少特征信息丢失并增强浅层特征利用率。提出了一种具有“参数共享”功能的高效检测头（E-Detect），以降低计算复杂性并提高检测速度，而不会造成显着的精度损失。实验结果表明，该模型在各个类别中均优于先进的缺陷检测算法。值得注意的是，对于小磨损、细微皱纹、倾斜的外盖和未对准的标签等具有挑战性的缺陷，它的准确率分别达到 89.9%、55.6%、89.4% 和 75%，提高了 3.2%、7%、2% 和 11.8分别超过基线模型的％。该模型的平均精度 (mAP) 也提高了 2.7%。