Li Yizhe, Xie Yidong, He Hu
State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Sensors (Basel). 2024 Nov 30;24(23):7691. doi: 10.3390/s24237691.
Aluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices. In this study, we propose an innovative aluminum surface defect detection method based on an optimized two-stage Faster R-CNN network. A 2D camera serves as the image sensor, capturing high-resolution images in real time. Optimized lighting and focus ensure that defect features are clearly visible. After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. The network was trained on a dataset of 3200 images captured by the image sensor, split in an 8:1:1 ratio for training, validation, and testing, respectively. The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection.
铝在航空航天、航空、汽车及其他行业中得到广泛应用。铝表面存在的缺陷对产品质量有重大影响。然而，传统检测方法无法满足工业实践的效率和准确性要求。在本研究中，我们提出了一种基于优化的两阶段更快区域卷积神经网络（Faster R-CNN）的创新型铝表面缺陷检测方法。二维相机作为图像传感器，实时捕捉高分辨率图像。优化的照明和对焦确保缺陷特征清晰可见。预处理后，图像被输入到一个融合了多尺度特征金字塔结构的深度学习网络中，该结构通过将高级语义信息与位置细节相结合，有效提高了缺陷识别的准确性。此外，我们引入了优化的卷积块注意力模块（CBAM）以进一步提高网络效率。此外，我们采用遗传K均值算法优化先验区域选择，并使用轻量级Ghost模型将网络复杂度降低了14.3%，这表明Ghost模型在训练和验证期间的损失函数优化以及检测准确性、速度和稳定性方面表现优异。该网络在由图像传感器捕获的3200张图像的数据集上进行训练，分别以8:1:1的比例划分为训练集、验证集和测试集。实验结果显示平均精度均值（mAP）为94.25%，单个精度均值（AP）值超过80%，符合缺陷检测的工业标准。
