Multi-scale target detection method and system for smoothly transmitting semantic information

Abstract

The invention provides a multi-scale target detection method and system for smoothly transmitting semantic information, relates to the technical field of computer vision, and aims to improve or prevent degradation of a deep network, completely and efficiently extract image features and make pixel classification more accurate. The method comprises the following steps: S1, constructing a residual block for placing an ReLU layer in front of a convolutional layer; s2, constructing a first-stage backbone network by adopting the residual blocks in the S1; s3, performing feature extraction on the image by using the first-stage backbone network to obtain a feature map; s4, inputting the feature map obtained in the S3 into a second-stage target detection network for processing to obtain a region suggestion; s5, mapping the region suggestion to the feature map to obtain a region of interest, and performing ROI Align processing to obtain a target detection result; in the second stage, the target detection network integrates a cross entropy loss function which introduces a balance coefficient; and the convolution layer of the residual block is subjected to feature image cutting and recombination operation in the convolution process.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a multi-scale target detection method and system for smoothly transmitting semantic information. Background technique [0002] In recent years, computer vision has flourished with the support of deep learning, and object detection has shown great practical and research value in many fields of computer vision. Many object detection methods proposed in the early years are based on artificially designed feature construction. The disadvantage of these detectors is their lack of versatility. Currently, more and more detection models based on convolutional neural network (CNN) have been proposed. By combining with CNNs, object detectors show powerful capabilities by handling complex tasks. There are two types of detectors: (1) one-stage method and (2) two-stage method. For object detectors using a two-stage approach, in the first stage, the detector tries to find a set of c...

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Problems solved by technology

Therefore, the detector is not robust enough and does not generalize well

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