Target detection method, device and storage medium

Abstract

The present invention provides a target detection method, device and storage medium. The method includes: initially detecting the target to be detected in the current frame image in the video data; combining the target to be detected with the previous frame of the current frame image At least one target in the image is matched; if there is a target that matches the target to be detected in the previous frame image, then according to the feature layer of the target to be detected in the current frame image and the current The feature layer in the first m frames of images of the frame images determines the category and location information of the target to be detected, where m is a positive integer. The target detection method, device and storage medium provided by the invention can not only reduce the difficulty of detection, but also improve the accuracy of detection.

Description

technical field [0001] The present invention relates to image detection technology, in particular to a target detection method, device and storage medium. Background technique [0002] The precision requirements for the detection of vehicles, pedestrians and other objects in the assisted driving of automobiles are very strict. The current detection technology is relatively accurate for rigid objects such as vehicles, traffic signs, and lane lines, while the detection accuracy for non-rigid objects such as pedestrians or bicycles is relatively low. [0003] At present, detection methods for pedestrians are mainly based on single-frame images in video streams, using traditional feature extraction and classification methods, or detection based on deep learning methods such as convolutional neural networks. Among them, the traditional method of feature extraction and classification is mainly to design the characteristics of pedestrians in advance, and then use machine learning ...

AI Technical Summary

Problems solved by technology

[0004] Due to the various deformations of pedestrians and other targets, in order to improve the detection accuracy when using the above methods for detection, it is necessary to expand the amount of data to include enough samples, and at the same time, it is necessary to increase the capacity of the model to cover possible deformations. Not only will it increase the difficulty of detection, but the accuracy of detection is not high

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