Multistage target detection method and model based on CNN multistage feature fusion

Abstract

The invention discloses a multi-stage target detection method and model based on CNN multistage characteristic fusion; the method mainly comprises the following steps: preparing a related image data set, and processing the data; building a base convolution nerve network (BaseNet) and Feature-fused Net model; training the network model built in the previous step so as to obtain a model of the corresponding weight parameter; using a special data set to finely adjust the trained detection model; outputting a target detection model so as to make target classification and identification, and providing a detection target frame and the corresponding precision. In addition, the invention also provides a multi-class target detection structure model based on CNN multistage characteristic fusion, thus improving the whole detection accuracy, optimizing model parameter values, and providing a more reasonable model structure.

Description

technical field [0001] The invention relates to the technical field of computational vision target detection, in particular to a CNN-based multi-level feature fusion multi-class target detection method and model. Background technique [0002] Target detection is a basic and very important research topic in the field of computational vision, involving image processing, machine learning, pattern recognition and many other disciplines. Its task is to classify and detect corresponding targets from images or videos to be processed. , and provide the specific position and accuracy information of the detected target. With the in-depth research and innovation of this technology, it has been widely used in automotive autonomous driving, video surveillance and analysis, face recognition, vehicle tracking and traffic flow statistics; and target detection is the basis for subsequent image analysis, understanding and application. Therefore, it has important research significance and app...

AI Technical Summary

Problems solved by technology

However, these classic target detection techniques generally have deficiencies: the targets in the image are often diverse in terms of pose, scale, aspect ratio, etc., and cannot detect multiple categories of targets of different sizes well, especially in complex scenes. When the target scale is changeable and the target scale is relatively small; because these model structures have the characteristics of hierarchical convolution downsampling, the feature information and position information extracted for some relatively small-scale targets are often lost, resulting in some high-semantic information of the target. The consequences of accurate positioning; in addition, the accuracy and efficiency of general target detection are still not well balanced

This method has the following shortcomings: (1) All the features of the convolutional layer are fused, and the relationship between the target size in the image and the high and low features output by the convolutional layer is not considered, that is, the low layer with high resolution is excessively combined Features and high-level features with high semantic information will increase unnecessary computational complexity; (2) The way of feature fusion is the key to the performance of small target detection, but it does not give the connection of multi-layer features to be fused The method is only to connect the output size with the output feature size of a certain convolutional layer; (3) This scheme does not provide a detection network model with appropriate speed and high accuracy for applying its method

However, the biggest problem with this method is that too many target regions of interest are added, resulting in too many irrelevant segment features, increasing the complexity, and not distinguishing the detection of different sizes of targets in the image. If the image contains a large number of relatively large The target increases the calculation amount of the target detection

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