Image detection technology for small defects in brake pad appearance based on few sample learning

In the image of minor defects in the appearance of brake pads, there may be significant differences in the number of different types of defects, resulting in a small sample size for some categories, making it difficult for the model to learn the characteristics of these categories of defects. To this end, a small sample learning based image detection technology for brake pad appearance defects is proposed. By using the Canny operator to obtain local maximum gradient amplitude pixels in the image of minor defects on the appearance of brake pads, edge detection of the defect image is achieved, and defect shape features are extracted. Using Boosting algorithm to construct a small sample learning model, calculating different weights for different training samples, to adjust the distribution of training samples for subsequent weak learners, in order to reduce overfitting and underfitting problems during the small sample learning process, and achieve image detection of small defects in the appearance of brake pads. The experimental results show that the method proposed in this paper has a high accuracy and recall rate for detecting small defects in the appearance of brake pads, including edge graying, uneven wear, cracking, and edge wear. Moreover, accurate detection of small defects can be achieved under small sample conditions.