A method for recognizing objects in an image based on depth learning

Abstract

The invention discloses a method for recognizing an object in an image based on depth learning, Here are the steps: an image is inputted, the candidate regions are extracted by convolution neural network, filter and optimize the output candidate region, At the same time, each candidate region is normalized, the candidate region is input to convolution neural network for feature extraction, and thetrained classification and regression network is used to classify, locate and detect the target image. Finally, the selected target region is subjected to border regression operation to correct the position of the target region. This method uses convolution neural network to extract the regions which may contain objects in the image, and reduces the number of candidate regions. At the same time,it optimizes the filtering operation on the output candidate regions of convolution neural network, which improves the computational speed of the algorithm. In addition, a variety of length-width ratio and region size are used for the candidate region of the target detection, which is closer to the real scene and improves the robustness of the algorithm.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a method for recognizing objects in images based on deep learning. Background technique [0002] The method of object detection in images based on deep learning is mainly used to identify objects in images. Common detection tasks are divided into three types: identification, positioning, detection, and segmentation. Recognition: Mainly divide the objects in the image into a category. Positioning: As the name implies, it is to detect the approximate position of the object in the image. The traditional method is to use a rectangle to frame the approximate position of the object in the image. Detection: not only to identify which objects are included in the image, but also to identify the approximate location of each object. Segmentation includes semantic segmentation and instance segmentation, which mainly solves the relationship between pixels in th...

AI Technical Summary

Problems solved by technology

However, real-life scenes are diverse, and factors such as lighting and environment make the appearance of objects in images very different. challenge

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