Research on the application of deep learning-based machine vision in automated inspection

The continuous updating of deep learning algorithms and theories has laid a solid foundation for the development of the machine vision field. Automated detection technology has become a hot topic of research in the field of machine vision in recent years. In this paper, we first introduce the traditional one-dimensional and two-dimensional image segmentation algorithm and then optimize the image segmentation algorithm by combining the conventional pigeon flocking algorithm and chaotic search algorithm so as to obtain a more accurate detection target image. Then, on the basis of a deep learning network, an attention mechanism is introduced to construct a Swin-Transformer image detection model to realize automatic detection of machine vision. Finally, the performance of the model is tested, and it is applied to watermelon seedling quality detection to explore its application value. The results show that in the performance test experiment of the image segmentation algorithm of this paper, the three indexes of F1, IoU, and accuracy of the image segmentation algorithm designed in this paper on the ISIC-2020 dataset are 93.90%, 93.74%, and 98.37%, which are ranked the first among the algorithms participating in the experiment. The precision, recall, and mAP values of the image detection model designed in this paper are 92.87%, 77.13%, and 83.21% on the experimental data test set, which are higher than those of other models participating in the experiment. The image detection model designed in this paper was practically applied to watermelon seedling quality detection. The accuracy of the model in detecting the presence or absence of diseased spots and cotyledon area, two key characteristic parameters of seedling quality, reached 100%. The model showed high reliability in practical application. The image segmentation algorithm and image detection model developed in this paper are highly useful in automated detection. © 2024 Ranran Zhao, published by Sciendo.