基于改进YOLOv5的铝锭合金表面缺陷检测技术研究

Improved YOLOv5-based surface defect detection technology for aluminum ingot alloys

针对铝锭合金表面缺陷在形态上不规则、检测效果欠佳的问题,提出了一种基于改进YOLOv5的铝锭合金表面缺陷检测方法。首先,利用Res2Net特征提取网络块替换基线模型中的CSPDarknet53模块,以实现多尺度缺陷的有效检测。其次,在YOLOv5的主干网络引入CBAM卷积注意力模块,以增强对缺陷特征的表征能力。最后,使用基于过参数化的重参数化卷积块替代主干和颈部网络的3×3卷积块,以降低模型的推理延时。与传统的目标检测方法进行对比实验,结果表明改进后的方法对缺陷检测的mAP达到75.8%,在检测精度和推理速度上均有显著提升,可很好地满足实际工业生产的任务和需求。

Aiming at the problems of irregular morphology and suboptimal detection performance of surface defect on aluminum ingot alloys,an improved YOLOv5-based defect detection method is proposed.Firstly,The Res2Net feature extraction network block is employed to replace the CSPDarknet53 module of the baseline model,which can effectively detect the multi-scale defect.Secondly,the CBAM convolutional attention module is introduced into the backbone network of YOLOv5 to enhance the representational ability of defect features.Finally,the over-parameterized reparameterization convolutional blocks are used to substitute for the 3×3 convolutional blocks in the backbone and neck networks so as to reduce the model's inference latency.Experimental results compared with the traditional target detection methods demonstrate the improved method achieves a mAP of 75.8%for defect detection,which is a significant improvement both in detection accuracy and inference speed,and can well satisfy the tasks and demands of practical industrial production.