Alex Noel Josephraj

Zhun Fan, Jiewei Lu, Benzhang Qiu et al.

Automated steel bar counting and center localization plays an important role in the factory automation of steel bars. Traditional methods only focus on steel bar counting and their performances are often limited by complex industrial environments. Convolutional neural network (CNN), which has great capability to deal with complex tasks in challenging environments, is applied in this work. A framework called CNN-DC is proposed to achieve automated steel bar counting and center localization simultaneously. The proposed framework CNN-DC first detects the candidate center points with a deep CNN. Then an effective clustering algorithm named as Distance Clustering(DC) is proposed to cluster the candidate center points and locate the true centers of steel bars. The proposed CNN-DC can achieve 99.26% accuracy for steel bar counting and 4.1% center offset for center localization on the established steel bar dataset, which demonstrates that the proposed CNN-DC can perform well on automated steel bar counting and center localization. Code is made publicly available at: https://github.com/BenzhangQiu/Steel-bar-Detection.

Zhun Fan, Zhongxing Li, Benzhang Qiu et al.

Object recognition and grasping plays a key role in robotic systems, especially for the autonomous robots to implement object sorting tasks in a warehouse. In this paper, we present a global texture-shape 3D feature descriptor which can be utilized in a system of object recognition and grasping, and can perform object sorting tasks well. Our proposed descriptor stems from the clustered viewpoint feature histogram (CVFH), which relies on the geometrical information of the whole 3D object surface only, and can not perform well in recognizing the objects with similar geometrical information. Therefore, we extend the CVFH descriptor with texture and color information to generate a new global 3D feature descriptor. The proposed descriptor is evaluated in tasks of recognizing and classifying 3D objects by applying multi-class support vector machines (SVM) in both public 3D image dataset and real scenes. The results of evaluation show that the proposed descriptor achieves a significant better performance for object recognition compared with the original CVFH. Then, the proposed descriptor is applied in our object recognition and grasping system, showing that the proposed descriptor helps the system implement the object recognition, object grasping and object sorting tasks well.