HOG features and connected region analysis-based workpiece object detection algorithm. (30th April 2020)

• Record Type: Journal Article
• Authors: Yu, Ting; Tian, Maoyi
• Is Part Of: International journal of innovative computing and applications. Volume 11:Number 2/3(2020)
• Page Start: 61
• Page End: 66
• Publication Date: 2020-04-30
• Subjects: workpiece object detection -- image gradient -- chromatography -- edge feature -- connected region -- meshing -- histogram; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In order to solve the problem of bearing workpiece object, namely, the insufficient detection ability of the algorithm caused by the complex edge features, a HOG features and connected region analysis-based workpiece object detection algorithm is proposed in this paper. Firstly, the target images of standard workpiece in the training set are meshed to calculate the pixel gradient in the grid, count the gradient histogram and complete the extraction and training of HOG features. Then interval division of the single peak threshold is refined, and a twothreshold segmentation mechanism is proposed to convert the two-valued image into a label image by combining the connected region analysis, and the evaluation of pixel attribute and the filtering of interference is conducted to achieve the purpose of accurately detecting the workpiece object. The experimental results show that the bearing workpiece object detection algorithm in this paper has higher accuracy and stability.