Tire defect detection algorithm based on multi-task learning and normal feature fusion

Jixiang Cheng; Andong Liu; Tian Tian

doi:10.1117/12.2631159

18 March 2022 Tire defect detection algorithm based on multi-task learning and normal feature fusion

Jixiang Cheng, Andong Liu, Tian Tian

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121681S (2022) https://doi.org/10.1117/12.2631159
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

The structure of tire X-ray image is complex, and the defect information is highly related to the background information, which brings difficulties to automatic defect detection. To solve these problems, this paper proposed a tire defect detection algorithm based on multi-task learning and normal feature fusion. Firstly, a tire defect detection model was designed based on YOLOV4, and then a background classification task was added into the model using the idea of multitask learning. Finally, the result of model recognition is adjusted by fusion background feature detection algorithm. Experimental results show that the proposed algorithm can greatly improve mAP when detecting several kinds of defects with a large relationship with location, and has good application value.

Citation Download Citation

Jixiang Cheng, Andong Liu, and Tian Tian "Tire defect detection algorithm based on multi-task learning and normal feature fusion", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121681S (18 March 2022); https://doi.org/10.1117/12.2631159

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KEYWORDS

Detection and tracking algorithms

Defect detection

Image fusion

Convolution

X-rays

X-ray imaging

Image processing

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