基于卷积神经网络的热轧钢条表面实时缺陷检测

热轧钢条的表面质量对成品至关重要,因此必须要严格控制热轧钢条的表面出现的缺陷。 针对当前 YOLOv4 算法检测 精度不高、对小范围信息表现较差等问题,提出一种改进 YOLOv4 自动检测方法。 首先,将 YOLOv4 中特征提取网络 CSPDarknet53 换为轻量级深层神经网络 MobileNetv3 来提高检测速度,并且加强对检测目标特征提取以及减少梯度消失问题。 其次,采用 K-Means 聚类生成适合本实验的先验框,有效提高学习效率,加快收敛速度。 最后,对置信度损失进行重新定义,提 出一种能够适应多尺度的损失函数,来解决因正负样本不平衡而导致检测效果差的问题。 实验结果表明,该方法较原 YOLOv4 模型在热轧钢条的表面缺陷检测上的均值平均精度值提高约 7. 94% ,速度提升约 4. 52 f / s,在保证检测速度的基础上有效提高 了精确度。

Real-time defect detection of hot rolling steel bar based on convolution neural network

It is important for the surface quality of hot rolled steel strips to make final product. Therefore, it is necessary to strictly control the defects on the surface of hot rolled steel strips. The current you only look once (YOLO) v4 algorithm has low detection accuracy and poor performance on small-scale information. To address these issues, an improved YOLOv4 automatic detection method is proposed. First, to improve detection speed, enhance detection target feature extraction and reduce gradient vanishing, the feature extraction network CSPDarknet53 in YOLOv4 is replaced with the lightweight deep neural network MobileNetv3. Secondly, to improve the learning efficiency and accelerate the convergence speed, the K-Means clustering is utilized to generate a prior box to suit for this experiment. Finally, the confidence loss is redefined and a loss function is proposed that can adapt to the multi-scale to solve the problem of poor detection effect due to the imbalance of positive and negative samples. Compared with the original YOLOv4 model for the surface defect detection of the hot rolled steel strip, experimental results show that the proposed method enhance the mean average precision and the speed about 7. 94% and 4. 52 f / s, respectively. The accuracy of this model is improved effectively while ensuring the detection speed.