An object detection method based on semantic segmentation enhancement

Abstract

The invention relates to an object detection method based on semantic segmentation enhancement, which comprises the following steps: preparing a labeled image; image set partition; deep convolution neural network structure based on semantic segmentation enhancement is designed for object detection. The deep convolution neural network based on semantic segmentation enhancement includes three main parts: backbone subnetwork, segmentation subnetwork and detection subnetwork. The backbone subnetwork is used to extract the general feature of the image, which is class-independent feature. The segmentation subnetwork extracts the features of semantic segmentation based on the backbone subnetwork, and predicts the segmentation heat map of each class of objects. The thermal map of each class of objects is used as a priori knowledge of the class, and is fused with the features extracted from the detection subnetwork to generate the class-related features. Each class of objects has the features of the corresponding class, and the features reflect the characteristics of the class of objects significantly. Model training.

Description

technical field [0001] The invention relates to a high-performance object detection method in the field of computer vision, in particular to a method for image object detection using a deep learning method. Background technique [0002] As a key technology in the development of artificial intelligence, deep learning technology has been widely used in many fields such as intelligent monitoring, human-computer interaction, assisted driving, and automatic driving to realize real-time detection and monitoring of people, cars, and other objects in the scene. identify. As an important implementation method in deep learning technology, deep convolutional neural network has achieved remarkable results in object detection tasks. [0003] Taking the autonomous driving system as an example, such as figure 1 As shown, in the object detection task, the video / image in the real scene is first captured by the vehicle camera; further, the video / image captured by the camera is input into th...

AI Technical Summary

Problems solved by technology

[0012] The purpose of the present invention is to overcome the problem of low detection accuracy of existing object detection algorithms based on deep convolutional neural networks, and propose a deep convolutional neural network object detection method based on semantic segmentation enhancement, which can effectively improve object detection The accuracy further promotes the application of object detection in many fields

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