Why deep learning

Deep learning can use more data or better algorithms to improve the results of learning algorithms. In terms of performance, deep learning explores the probability space of neural networks. Compared with other tools, deep learning algorithms are more suitable for unsupervised and semi supervised learning. It is more suitable for strong feature extraction and is also more suitable for the fields of image recognition, text recognition, speech recognition, etc.

Basic Core Algorithm for Defect Detection


The task of object detection is to identify objects of interest in images or videos, while detecting their position and size, which is one of the core issues in the field of machine vision. There are many uncertain factors in the process of object detection, such as the uncertainty of the number of objects in the image, the different appearances, shapes, and postures of objects, and the interference of lighting, occlusion, and other factors during object imaging, which makes the detection algorithm somewhat difficult. The object detection algorithm has also shifted from traditional algorithms based on manual features to detection techniques based on deep neural networks.


Image segmentation is an important component of many visual understanding systems. It includes dividing an image (or video frame) into multiple segments or objects. Due to the success of deep learning models in visual applications, a significant amount of work has been devoted to developing image segmentation methods using deep learning models, including fully convolutional pixel labeling networks, encoder decoder architectures, multi-scale and pyramid based methods, recursive networks, visual attention models, and generating adversarial models.


The core of image classification is the task of assigning a label to an image from a given classification set. Convolutional neural networks are a special type of neural network that has the ability to extract unique image features from image data. At present, convolutional neural networks have been widely used in face detection and recognition, as they are very helpful in identifying complex features in image data.