Niu Wei, Lv Cheng, Zhang Enxu, Wei Zhongbin
School of Mechanical Engineering, Xijing College, Xi'an, China.
PLoS One. 2025 Jul 18;20(7):e0328815. doi: 10.1371/journal.pone.0328815. eCollection 2025.
As the core components of permanent magnet motors, the surface defects of magnetic tiles can directly affect the working performance of the motors. There are various types of defects in magnetic tiles, such as chipping and wear. Traditional magnetic tile defect detection mainly relies on manual inspection and faces challenges like low detection accuracy and high cost. Moreover, most defects on magnetic tile surfaces are located on curved surfaces, leading to an uneven distribution of defects and tiny features, which makes accurate defect localization challenging. To solve these problems, this study proposes a novel magnetic tile defect detection algorithm called YOLO-RDM. First, we apply DOConv to the neck network. By using a lightweight convolution method, we replace the traditional convolution in the original network, thereby improving the feature extraction ability of the model and achieving lightweight processing. Second, we design an RPA Block to improve the C2f module. By introducing a parallel attention mechanism, we enhance the feature extraction ability of the algorithm. Finally, we replace the original backbone network of YOLOv8 with the MogaNet network. MogaNet is a module that aggregates contextual information, enhancing the network's discriminative power, learning efficiency, and ability to capture defect features in images. The experimental results show that the mean average precision (mAP@0.5) of the improved model reaches 95.0%, which is 4.8% higher than that of the original model, and its inference time is less than 5.6 ms. It also has obvious performance advantages compared with other object detection models. In addition, it achieves good recognition results on the NEU metal surface defect dataset. It can be proven that the YOLO-RDM model has strong recognition and generalization abilities and can be used in practical applications of magnetic tile defect detection.
作为永磁电机的核心部件，磁瓦的表面缺陷会直接影响电机的工作性能。磁瓦存在多种类型的缺陷，如碎裂和磨损。传统的磁瓦缺陷检测主要依靠人工检查，面临检测精度低和成本高的挑战。此外，磁瓦表面的大多数缺陷位于曲面上，导致缺陷分布不均匀且特征微小，这使得准确的缺陷定位具有挑战性。为了解决这些问题，本研究提出了一种名为YOLO-RDM的新型磁瓦缺陷检测算法。首先，我们将DOConv应用于颈部网络。通过使用轻量级卷积方法，我们替换了原始网络中的传统卷积，从而提高了模型的特征提取能力并实现了轻量级处理。其次，我们设计了一个RPA模块来改进C2f模块。通过引入并行注意力机制，我们增强了算法的特征提取能力。最后，我们用MogaNet网络替换了YOLOv8的原始骨干网络。MogaNet是一个聚合上下文信息的模块，增强了网络的辨别能力、学习效率和捕捉图像中缺陷特征的能力。实验结果表明，改进模型的平均精度均值（mAP@0.5）达到95.0%，比原始模型高4.8%，其推理时间小于5.6毫秒。与其他目标检测模型相比，它也具有明显的性能优势。此外，它在NEU金属表面缺陷数据集上取得了良好的识别结果。可以证明，YOLO-RDM模型具有很强的识别和泛化能力，可用于磁瓦缺陷检测的实际应用。
