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A novel robust algorithm for position and orientation detection based on cascaded deep neural network
Lin Weiyang
Ren Xinyang
Zhou Tiantian
Cheng Xiaojing
Tong Mingsi
Neurocomputing, 2018, 308: 138-146.
Summary
Estimating position and orientation of the object by using machine vision is essential in industrial automation. The traditional canny operator and Hough transform edge detection algorithm is widely used, but its accuracy and real-time object recognition in complex backgrounds are very limited. Other algorithms such as SVM and BP network are usually inaccurate for regression issues. In this paper, the method of a cascade of convolution networks is proposed which results in high precision pose estimates. SSD is utilized to obtain the bounding box of the object to narrow down the recognition range. Convolution neural network is utilized to detect the orientation of the object. This method can extract weak features of the sample image. In generally, the proposed method possess a greatly improved accuracy and recognition rate compared with the traditional algorithm.
Keywords
Object detection; Convolution neural network; Mixed deep neural network
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