摘要
为了提高鼓形滚子表面微小瑕疵缺陷检测的精确率和召回率,增强模型对小目标缺陷的检测能力,针对YOLOv8s网络,提出细粒化卷积模块SPD-Conv来代替卷积下采样,细粒化地提取小缺陷的特征.在特征融合模块,引入GFPN特征融合模块,增强相邻层级间的跨尺度连接和同尺度下的跨层连接,有助于小目标特征信息在卷积网络的传递.在头部增加小目标检测层,提高模型对小缺陷的检测能力.在损失函数方面,利用动态非单调聚焦的Wise-IOU的边界框损失函数替换CIOU,在加快网络收敛的同时,提高网络检测的精度.在自制的鼓形滚子缺陷数据集上进行测试,结果表明,改进的YOLOv8s在倒角数据集、侧面数据集、端面数据集的mAP@0.5分别达到0.911、0.983、0.935,相比于YOLOv8s,mAP@0.5分别提高了 6.4％、3.3％、4％,精确度和召回率也有一定的提升,平均每张图片的检测时间为23ms.与原模型相比,改进的YOLOv8s对小目标缺陷有更好的定位能力和检测精度,检测速度能够满足工业大批量检测的要求.
Abstract
A fine-grained convolution module SPD-Conv was proposed to replace the convolution subsampling for YOLOv8s network and extract the features of small defects in a fine-grained way in order to improve the accuracy and recall rate of the detection of small defects on the surface of drum rollers and enhance the detection ability of the model for small target defects.GFPN feature fusion module was introduced to enhance the cross-scale connection between adjacent layers and cross-layer connection under the same scale in the feature fusion module,which is conducive to the transmission of small target feature information in the convolutional network.The small target detection layer was added to the head in order to improve the detection ability of the model.The boundary frame loss function of Wise-IOU was used to replace CIOU in terms of loss function,which could accelerate network convergence and improve the accuracy of network detection.The test was conducted on the self-made drum roller defect dataset.Results showed that the improved YOLOv8s achieved 0.911,0.983 and 0.935 in the chamfer dataset,side dataset and end dataset,respectively.mAP@0.5 increased by 6.4％,3.3％and 4％respectively compared with YOLOv8s.Accuracy and recall rates have improved with an average detection time of 23 ms per image.The improved YOLOv8s has better localization ability and detection accuracy for small target defects compared with the original model,and the detection speed can meet the requirements of industrial mass detection.
基金项目
国家自然科学基金-浙江两化融合联合基金(U1809221)
宁波泛3315人才项目()
维普
万方数据