基于深度学习框架的装配机器人零件实时检测方法

Real-time Detection of Parts by Assembly Robot Based on Deep Learning Framework

针对工业生产线上装配机器人在粘连、堆叠、光照变化及环境因素干扰等复杂条件下零件检测率低、鲁棒性差等问题,提出一种基于改进YOLOv3深度学习框架的零件实时检测方法。在基础特征提取网络Darknet-53的每个残差网络后嵌入CFE模块,构建融合CFE模块和Darknet-53的深度特征提取网络CFE-Darknet53,建立YOLOv3深度学习框架下基于CFE-Darknet53的零件实时检测模型,提升检测网络在复杂环境下特征提取能力;设计一种改进K-means算法来预测边界框,通过对零件数据集进行聚类分析,选取最优的锚框个数和尺寸,进一步提高检测准确性。实验结果表明:在复杂条件下,改进算法对相似度很高的多类零件检测准确率能达到91.6%以上,相比YOLOv3算法提升了近10%以上;检测时间为43 ms,在视频传输帧率(24帧/s)下实现了零件实时准确检测。

For the problems about low accuracy and poor robustness under complex conditions such as adhesion,stacking,illumination change and environmental factors,a part real-time detection method based on improved YOLOv3 deep learning framework is proposed.The CFE module is embedded in each residual network of Darknet-53 basic feature extraction network.A deep feature extraction network CFE-Darknet53 that combines CFE module and Darknet-53 is constructed.A real-time part detection model based on CFE-Darknet53 under the YOLOv3 deep learning framework is established,which improves the feature extraction ability of the detection network in complex environment.An improved K-means algorithm is designed to predict the bounding box.The data sets of parts are clustered to select the optimal number and size of anchor frames,which further improves the detection accuracy.The experimental results show that the detection accuracy of the improved YOLOv3 algorithm for multi class parts with high similarity can reach 91.6%under complex conditions.Compared with the YOLOv3 algorithm,the improved YOLOv3 algorithm improves the detection accuracy by more than 10%.The detection time is 43 ms,so the improved method achieves real-time detection of parts at the video transmission frame rate of 24 frames/s.