The complex background on the car body surface, such as the orange peel-like texture and shiny metallic powder, poses a considerable challenge to automated defect detection. Two mainstream methods are currently used to tackle this challenge: global information-based and attention mechanism-based methods. However, these methods lack the capability to integrate valuable global-to-local information and explore deeper distinguishable features, thereby affecting the overall detection performance. To address this issue, we propose a novel attention enhanced global–local refined detection network (AEGLR-Net), which can perform effective global-to-local refined feature extraction and fusion. First, we design an adaptive Transformer–CNN tandem backbone (ATCT-backbone) to dynamically aware valuable global information and integrate local details to comprehensively extract specific features between defects and complex backgrounds. Then, we propose a novel refined cross-dimensional aggregation (RCDA) attention to facilitate the point-to-point interaction of multidimensional information, effectively emphasizing the representation of deeper discriminative defect features. Finally, we construct an attention-embedded flexible feature pyramid network (AE-FFPN), which incorporates the RCDA attention to guide the feature pyramid network in targeted feature fusion, thereby enhancing the efficiency of feature fusion in the detection model. Extensive comparative experiments demonstrate that the AEGLR-Net outperforms state-of-the-art approaches, attaining exceptional performance with 89.2 % mAP (mean average precision) and 85.5 FPS (frames per second).

中文翻译：

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AEGLR-Net：注意力增强的全局-局部精细化网络，用于精确检测车身表面缺陷


车身表面的复杂背景，如橙皮状纹理和闪亮的金属粉末，对自动缺陷检测提出了相当大的挑战。目前有两种主流方法来应对这一挑战：基于全局信息的方法和基于注意力机制的方法。然而，这些方法缺乏整合有价值的全局到局部信息并探索更深层次的可区分特征的能力，从而影响了整体检测性能。为了解决这个问题，我们提出了一种新颖的注意力增强型全局-局部精细化检测网络（AEGLR-Net），它可以执行有效的全局到局部精细化特征提取和融合。首先，我们设计了一个自适应 Transformer-CNN 串联主干（ATCT-backbone）来动态感知有价值的全局信息并整合局部细节，以全面提取缺陷和复杂背景之间的特定特征。然后，我们提出了一种新颖的细化跨维聚合（RCDA）注意力，以促进多维信息的点对点交互，有效地强调更深层次的判别性缺陷特征的表示。最后，我们构建了一个注意力嵌入的灵活特征金字塔网络（AE-FFPN），它结合了RCDA注意力来引导特征金字塔网络进行目标特征融合，从而提高了检测模型中特征融合的效率。大量的比较实验表明，AEGLR-Net 的性能优于最先进的方法，获得了 89.2% mAP（平均精度）和 85.5 FPS（每秒帧数）的卓越性能。