Target vision detection is a very important research issue in the field of computer vision. With the application of electronic devices becoming more and more common in social production and people's lives, digital images have become an indispensable information medium, producing massive amounts of image data every moment. At the same time, it is becoming more and more important to accurately identify the objects in the image. The following Ruishi special technology to analyze the depth of learning in the target visual inspection system application.
We not only pay attention to the simple classification of images, but also hope to accurately obtain the objects of interest and their locations in the image, and apply this information to a series of real-world tasks such as video surveillance, autonomous driving, and human-computer interaction. Therefore, the target vision Detection technology has received extensive attention.
Traditional target vision detection technology
The traditional target vision detection technology is roughly divided into three steps in the process: Region proposal, Feature representation and Region classification, as shown in FIG. 1 . The basic flow is adopted by many tasks. They adopt different processing strategies in the target area proposal, image feature representation, and candidate area classification. In recent years, with the development of deep learning technology, many target-based visual detection algorithms based on deep learning have been proposed one after another, which are significantly superior to traditional methods in terms of accuracy and have become the latest research hotspot.
Target learning detection algorithm based on deep learning
The deep learning model has powerful characterization and modeling capabilities. Through supervised or unsupervised training methods, the target feature representation can be learned layer by layer and automatically, and the abstraction and description of the object hierarchy can be realized. In the field of image recognition, Krizhevsky et al. (2012) built a deep convolutional neural network (CNN) in 2012 and achieved great success in large-scale image classification tasks. This has led to a high degree of attention to the CNN model and thus to the advancement of target detection research. . This article first introduced the classic AlexNet image classification and its improved models ZFNet, VGG, GoogLeNet, ResNet and so on. As the model gets deeper and deeper, the Top-5 error rate for image classification is getting lower and lower, and it has now fallen below 3%. As with image classification, the target detection input is also the entire image, and they have a great deal of similarity in feature representation and classifier design. By adopting these CNN models to obtain a more powerful feature representation and then applying it to the target detection task, higher detection accuracy can be obtained. This paper introduces the research status of deep learning in the visual inspection of the target from two aspects: the method based on regional suggestions and the method without regional advice, and summarizes the published experimental results for quantitative comparison.
Finally, the difficulties and challenges in applying deep learning methods to target vision inspection are discussed. For example, the theory of deep learning is still not perfect, and the large-scale diversity dataset is still lacking. In order to solve these problems, we think that parallel vision can be used for research. The online optimization of the visual system through “parallel execution†can stimulate the potential of deep learning. We believe that the combination of deep learning and parallel vision will inevitably push forward the research and application of target vision detection.
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