面向牵引座焊缝表面质量检测的轻量型深度学习算法

Lightweight deep learning algorithm for weld seam surface quality detection of traction seat

针对人工和传统自动化算法检测牵引座焊缝表面存在检测精度低、速度低的问题,提出一种轻量型的牵引座焊缝表面质量检测算法YOLOv5s-G2CW。首先,用GhostBottleneckV2模块替换YOLOv5s中的C3模块以降低模型的参数量;其次,在YOLOv5s模型的Neck部分引入CBAM(Convolutional Block Attention Module),在通道和空间两个维度上融合焊缝特征;然后将YOLOv5s的定位损失函数改进为Wise-IoU以聚焦普通质量锚框的预测回归;最后移除YOLOv5s模型中用于大物体检测的13×13特征层以进一步降低模型的参数量。实验结果表明,与YOLOv5s模型相比,YOLOv5s-G2CW的模型大小减小了53.9%,帧率提高了8.0%,平均精度均值(mAP)提高了0.8个百分点,能够满足牵引座焊缝表面质量检测的准确性和实时性要求。

In order to address the low accuracy and speed of detection by manual and traditional automation methods for the weld seam surface of traction seat,a lightweight weld seam quality detection algorithm YOLOv5s-G2CW was proposed for the weld seam surface of traction seat.Firstly,the GhostBottleneckV2 module was applied as a replacement for the C3 module in YOLOv5s to reduce the number of parameters used in the model.Then,the CBAM(Convolutional Block Attention Module)was introduced into the Neck of the YOLOv5s model for integration of the weld features in two dimensions:channel and space.Also,the positioning loss function of the YOLOv5s model was improved into Wise-IoU,focusing on the predictive regression of ordinary quality anchor frames.Finally,the 13×13 feature layer used for the detection of large-sized objects in the YOLOv5s model was removed to further reduce the number of parameters used in the model.Experimental results show that,compared with the YOLOv5s model,the size of YOLOv5s-G2CW model reduces by 53.9%,the number of frames transmitted per second increases by 8.0%,and the mAP(mean Average Precision)value increases by 0.8 percentage points.It can be seen that the model is applicable to meet the requirements for real-time and accurate detection of the weld seam surface for traction seat.