Abstract
Cylinder liners are important to automobile engines. The appearance quality will directly affect the life and safety of the engines. At present, the appearance quality inspection of cylinder liners mainly relies on manual visual judgment, which is easily affected by the subjective factors of inspectors. This paper studies improved machine vision to realize surface defect detection. It proposes the improvement of the attention mechanism and a feature fusion method to locate and classify the defect. Experiments show that the method proposed in this paper has improved both accuracy and speed, and it can detect defects in production and realize industrialization. At the same time, the method studied in this paper has the value of popularization and application for appearance defect detection in other fields.
Highlights
Cylinder liners are important to automobile engines. e appearance quality will directly affect the life and safety of the engines. is paper proposes the improvement of the attention mechanism and a feature fusion method to locate and classify the defect, which has improved both accuracy and speed. e surface defect will indicate that the cylinder liner has a major internal quality problem, which may cause the internal combustion engine to work abnormally and cause safety problems
The inspection of the surface quality of cylinder liners mainly relies on manual inspection methods
Is paper proposes a deep learning-based defect detection method to realize surface defect detection. e content tested in this paper is the “raised” and “unsintered” surface defects of the nonburr cylinder liner, which are defined as follows: Unsintered: the unsintered shape that appears on the surface of the thornless cylinder liner is generally stripshaped. e length of the defect must not exceed 10 mm and the width must not exceed 5 mm. ere are multiple unsintered shapes within the same field of view. e distance must be more than 10 mm, otherwise, no matter how small its size is, it will be regarded as a defect
Summary
Cylinder liners are important to automobile engines. e appearance quality will directly affect the life and safety of the engines. is paper proposes the improvement of the attention mechanism and a feature fusion method to locate and classify the defect, which has improved both accuracy and speed. e surface defect will indicate that the cylinder liner has a major internal quality problem, which may cause the internal combustion engine to work abnormally and cause safety problems. E surface defect will indicate that the cylinder liner has a major internal quality problem, which may cause the internal combustion engine to work abnormally and cause safety problems. The inspection of the surface quality of cylinder liners mainly relies on manual inspection methods. Manual inspection methods are unable to meet production requirements, especially for small defects. Traditional machine vision inspection algorithms are less flexible in feature extraction, and feature extraction algorithms need to be constructed with the types of defects. Compared with traditional machine vision algorithms, deep learning algorithms show higher stability and adaptability when facing changing scenes and targets and have higher detection accuracy [1]. Is paper proposes a deep learning-based defect detection method to realize surface defect detection. When the diameter exceeds 5 mm, it will be regarded as a defect, and no more than 3 bumps are allowed within the same field of view
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