Purpose

Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD).

Design/methodology/approach

Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features.

Findings

Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects.

Originality/value

This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

中文翻译：

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一种基于多特征融合SSD的快速工件检测方法

目的

工件分拣是工业生产线中的关键环节。基于视觉的工件分拣系统是非接触式的，适用范围广。工件的检测与识别是工件分拣系统的关键技术。为将深度学习算法引入工件检测中，提高检测精度，本文旨在提出一种基于单次多盒检测器（SSD）的工件检测算法。

设计/方法/方法

提出了一种用于快速工件检测的多特征融合 SSD 网络。首先，将工件的多视图 CAD 渲染图像用作深度学习数据集。其次，训练视觉几何组网络进行工件识别，以识别工件的类别。第三，本研究设计了一种多级特征融合方法来提高SSD的检测精度（尤其是对小物体）；具体来说，增加了一个特征融合模块，它使用“element-wise sum”和“concatenation operation”来结合浅层特征和深层特征的信息。

发现

实验结果表明，该方法实际工件检测准确率可达96%，速度可达41帧/秒。与原SSD相比，该方法提高了7%的准确率，提高了小物体的检测性能。

原创性/价值

本文创新性地将SSD检测算法引入到工业场景的工件检测中，并对其进行了改进。添加了特征融合模块，将浅层特征和深层特征的信息结合起来。多特征融合SSD网络证明了将深度学习算法引入工件分拣的可行性和实用性。