A Convolutional Neural Network Object Detection Method Based on Pyramid Input Gain

Abstract

The invention relates to a target detection method of a convolutional neural network based on pyramid input gain, and belongs to the technical field of computer vision and target detection. The target detection method is based on the convolutional neural network model PiaNet comprising a feature extraction module and a multi-task prediction module; the target detection method includes a training phase and a testing phase; the training phase adopts a two-stage migration learning strategy, comprising: step (1 ) data enhancement and data preprocessing to produce the training set of the first stage training, the training set and the test set of the second stage training; step (2) carry out the first stage training in the two classification network; step (3) carry out the second stage Stage training to obtain the trained PiaNet network; the test stage is to accurately detect the target, specifically: input the test set into the trained PiaNet network, and output the detection frame position and classification results through the multi-task loss function. It has wide applicability and high robustness.

Description

technical field [0001] The invention relates to a target detection method of a convolutional neural network based on pyramid input gain, and belongs to the technical field of computer vision and target detection. Background technique [0002] Target detection refers to finding the location information such as the specific position and size of all interested targets in the image. This problem is one of the basic problems in the fields of computer vision and pattern recognition. It is widely used in applications such as monitoring and analysis, face recognition, and nodule or tumor detection in medical CT images. [0003] Existing object detection methods are mainly divided into two categories: [0004] 1) Traditional object detection methods. Traditional target detection generally adopts the sliding window framework, which mainly includes steps such as image space segmentation, feature design and extraction, and classification recognition. It needs to search in several dime...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to propose a method for target detection based on a convolutional neural network based on pyramidal input gain, aiming at the technical defects of low reliability and high rate of missed detection in the existing method for target detection based on convolutional neural networks. The above method proposes a convolutional neural network model PiaNet based on pyramid input gain. PiaNet combines multi-scale processing and multi-task learning to effectively improve detection accuracy and detection accuracy.

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