Attention Deeplabv3 model and its application into gear pitting measurement

Article type: Research Article

Authors: Xi, Dejun | Qin, Yi^{; *} | Wang, Zhiwen

Affiliations: Chongqing University, Institute of Mechanical and Vehicle Engineering, Chongqing, China

Correspondence: [*] Corresponding author. Yi Qin, Chongqing University, Institute of Mechanical and Vehicle Engineering, postal code 400044, Chongqing, China. E-mail: [email protected].

Abstract: An efficient visual detection method is explored in this study to address the low accuracy and efficiency of manual detection for irregular gear pitting. The results of gear pitting detection are enhanced by embedding two attention modules into Deeplabv3 + to obtain an improved segmentation model called attention Deeplabv3. The attention mechanism of the proposed model endows the latter with an enhanced ability for feature representation of small and irregular objects and effectively improves the segmentation performance of Deeplabv3. The segmentation ability of attention Deeplabv3+ is verified by comparing its performance with those of other typical segmentation networks using two public datasets, namely, Cityscapes and Voc2012. The proposed model is subsequently applied to segment gear pitting and tooth surfaces simultaneously, and the pitting area ratio is calculated. Experimental results show that attention Deeplabv3 has higher segmentation performance and measurement accuracy compared with the existing classical models under the same computing speed. Thus, the proposed model is suitable for measuring various gear pittings.

Keywords: Image segmentation, Deeplabv3+, attention mechanism, feature expression, gear pitting

DOI: 10.3233/JIFS-210810

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3107-3120, 2022