Wang Hongping, Chen Gong, Rong Xin, Zhang Yiwen, Song Linsen, Shang Xiao
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130012, China.
Faw Tooling Die Manufacturing Co., Ltd., Lvyuan, Changchun 130013, China.
Sensors (Basel). 2024 Aug 21;24(16):5392. doi: 10.3390/s24165392.
The stator of a flat wire motor is the core component of new energy vehicles. However, detecting quality defects in the coating process in real-time is a challenge. Moreover, the number of defects is large, and the pixels of a single defect are very few, which make it difficult to distinguish the defect features and make accurate detection more difficult. To solve this problem, this article proposes the YOLOv8s-DFJA network. The network is based on YOLOv8s, which uses DSFI-HEAD to replace the original detection head, realizing task alignment. It enhances joint features between the classification task and localization task and improves the ability of network detection. The LEFG module replaces the C2f module in the backbone of the YOLOv8s network that reduces the redundant parameters brought by the traditional BottleNeck structure. It also enhances the feature extraction and gradient flow ability to achieve the lightweight of the network. For this research, we produced our own dataset of stator coating quality regarding flat wire motors. Data augmentation technology (Gaussian noise, adjusting brightness, etc.) enriches the dataset, to a certain extent, which improves the robustness and generalization ability of YOLOv8s-DFJA. The experimental results show that in the performance of YOLOv8s-DFJA compared with YOLOv8s, the mAP@.5 index increased by 6.4%, the precision index increased by 1.1%, the recall index increased by 8.1%, the FPS index increased by 9.8FPS/s, and the parameters decreased by 3 Mb. Therefore, YOLOv8s-DFJA can be better realize the fast and accurate detection of the stator coating quality of flat wire motors.
扁线电机的定子是新能源汽车的核心部件。然而，实时检测涂层过程中的质量缺陷是一项挑战。此外，缺陷数量众多，单个缺陷的像素很少，这使得难以区分缺陷特征，更难以进行准确检测。为了解决这个问题，本文提出了YOLOv8s-DFJA网络。该网络基于YOLOv8s，使用DSFI-HEAD替换原始检测头，实现任务对齐。它增强了分类任务和定位任务之间的联合特征，提高了网络检测能力。LEFG模块取代了YOLOv8s网络主干中的C2f模块，减少了传统瓶颈结构带来的冗余参数。它还增强了特征提取和梯度流能力，以实现网络的轻量化。对于本研究，我们制作了自己的关于扁线电机定子涂层质量的数据集。数据增强技术（高斯噪声、调整亮度等）在一定程度上丰富了数据集，提高了YOLOv8s-DFJA的鲁棒性和泛化能力。实验结果表明，与YOLOv8s相比，YOLOv8s-DFJA的性能中，mAP@.5指标提高了6.4%，精确率指标提高了1.1%，召回率指标提高了8.1%，FPS指标提高了9.8FPS/s，参数减少了3 Mb。因此，YOLOv8s-DFJA能够更好地实现扁线电机定子涂层质量的快速准确检测。
