Affiliations: [a]
Key Laboratory of Advanced Manufacturing and Automation Technology of Guangxi University (Guilin University of Technology), Guilin, China
| [b]
School of Computer and Information Engineering, Fuyang Normal University, FuYan, China
Correspondence:
[*]
Corresponding author. Huai’an Yi, Key Laboratory of Advanced Manufacturing and Automation Technology of Guangxi University (Guilin University of Technology), Guilin, China, 541006. E-mail: [email protected]
Abstract: The surface roughness of the workpiece is one of the important indicators to measure the quality of the workpiece. Vision-based detection methods are mainly based on human-designed image feature indicators for detection, while the self-extraction method of milling surface features based on deep learning has problems such as poor perception of details, and will be affected by surface rust. In order to solve these problems, this paper proposes a visual inspection method for surface roughness of milling rusted workpieces combined with local equilibrium histogram and CBB-yolo network. Experimental results show that local equilibrium histogram can enhance the milling texture and improve the accuracy of model detection when different degrees of rust appear on the surface of the milled workpiece. The detection accuracy of the model can reach 97.9%, and the Map can reach 99.3. The inference speed can reach 29.04 frames per second. And the inspection of workpieces without rust, this method also has high detection accuracy, can provide automatic visual online measurement of milling surface roughness Theoretical basis.
Keywords: Surface roughness detection, CBB-yolo, milling workpieces, local equilibrium histosquare
