Wen Liwei, Li Shihao, Ren Jiajun
College of Material Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Haiying Aerospace Materials Research Institute (Suzhou) Co., Ltd., Suzhou 215100, China.
Materials (Basel). 2023 Jul 27;16(15):5291. doi: 10.3390/ma16155291.
To address the issues of low detection accuracy, slow detection speed, high missed detection rate, and high false detection rate in the detection of surface defects on pre-impregnated composite materials during the automated tape laying and winding process, an improved YOLOv5 (You Only Look Once version 5) algorithm model was proposed to achieve the high-precision, real-time detection of surface defects. By leveraging this improvement, the necessity for frequent manual interventions, inspection interventions, and subsequent rework during the automated lay-up process of composite materials can be significantly reduced. Firstly, to improve the detection accuracy, an attention mechanism called "CA (coordinate attention)" was introduced to enhance the feature extraction ability, and a Separate CA structure was used to improve the detection speed. Secondly, we used an improved loss function "SIoU (SCYLLA-Intersection over Union) loss" to replace the original "CIoU (Complete-Intersection over Union) loss", which introduced an angle loss as a penalty term to consider the directional factor and improve the stability of the target box regression. Finally, Soft-SIoU-NMS was used to replace the original NMS (non-maximum suppression) of YOLOv5 to improve the detection of overlapping defects. The results showed that the improved model had a good detection performance for surface defects on pre-impregnated composite materials during the automated tape laying and winding process. The FPS (frames per second) increased from 66.7 to 72.1, and the mAP (mean average precision) of the test set increased from 92.6% to 97.2%. These improvements ensured that the detection accuracy, as measured by the mAP, surpassed 95%, while maintaining a detection speed of over 70 FPS, thereby meeting the requirements for real-time online detection.
为了解决自动铺带和缠绕过程中预浸复合材料表面缺陷检测中存在的检测精度低、检测速度慢、漏检率高和误检率高等问题，提出了一种改进的YOLOv5（You Only Look Once版本5）算法模型，以实现对表面缺陷的高精度实时检测。通过利用这一改进，可以显著减少复合材料自动铺层过程中频繁的人工干预、检查干预和后续返工的必要性。首先，为了提高检测精度，引入了一种名为“CA（坐标注意力）”的注意力机制来增强特征提取能力，并使用了一种分离的CA结构来提高检测速度。其次，我们使用了一种改进的损失函数“SIoU（SCYLLA-交并比）损失”来取代原来的“CIoU（完全交并比）损失”，该损失函数引入了角度损失作为惩罚项，以考虑方向因素并提高目标框回归的稳定性。最后，使用Soft-SIoU-NMS来取代YOLOv5原来的NMS（非极大值抑制），以改进对重叠缺陷的检测。结果表明，改进后的模型在自动铺带和缠绕过程中对预浸复合材料表面缺陷具有良好的检测性能。每秒帧数（FPS）从66.7提高到72.1，测试集的平均精度均值（mAP）从92.6%提高到97.2%。这些改进确保了以mAP衡量的检测精度超过95%，同时保持超过70 FPS的检测速度，从而满足实时在线检测的要求。
