Target detection method based on multi-feature fusion of full convolutional network

Abstract

The invention designs a target detection method based on multi-feature fusion of a full convolutional network. The main technical features are that constructing a full convolutional neural network having six convolutional layer groups; using the first five groups of convolutional layers of the convolutional neural network to extract image features, and outputting the image features to be fused toform a fusion feature chart; performing convolutional processing on the fused feature map to directly generate a fixed number of target frames of different sizes; and calculating a classification error and positioning error between the target frames generated by the convolutional neural network and real frames, utilizing a random gradient descent method to reduce a training error to obtain parameters of a final training model, and finally carrying out a test to obtain a target detection result. The target detection method based on multi-feature fusion of the full convolutional network utilizesa powerful capability of representing a target of a deep convolutional network, constructs the full convolutional neural network used for target detection, proposes a new fusion feature method, improves detection speed and precision of an algorithm, and obtains a good target detection result.

Description

technical field [0001] The invention belongs to the technical field of target detection, in particular to a target detection method based on multi-feature fusion of a fully convolutional network. Background technique [0002] As one of the important research topics of computer vision, target detection is widely used in various fields such as national defense and military, public transportation, social security and commercial applications. Therefore, the research on target detection algorithm has very important military and commercial value. The so-called target detection is to identify the target by analyzing the characteristics of the target in the image or video, obtain the category and location information of the target, and provide help for further analysis and understanding of the target, such as target tracking and analysis based on image content, etc. . However, targets usually have variable factors such as different shapes, colors, brightness, and occlusions, and t...

AI Technical Summary

Problems solved by technology

[0004] Although the target detection algorithm has achieved good results after decades of development, and the emergence of convolutional neural networks has greatly improved the target detection accuracy, many problems still need to be improved, for example, how to balance accuracy and speed, improve Small target detection accuracy, reduce target positioning error, etc.

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