Image target detection method and device and storage medium

Abstract

The embodiment of the invention discloses an image target detection method and device and a storage medium, and the method comprises the steps: obtaining a to-be-detected image, carrying out the zooming of the to-be-detected image according to different resolutions, and obtaining a plurality of zoomed images; respectively screening an area conforming to the type of the target area from each zoomedimage to obtain a plurality of initial image blocks; dividing each initial image block into a plurality of regions, and obtaining the probability that each region belongs to a target region; extracting an area of which the probability is greater than a preset threshold from each initial image block to obtain a plurality of candidate image blocks; and mapping the plurality of candidate image blocks to the to-be-detected image, and screening out an area where the candidate image blocks conforming to a preset condition are located according to the overlap ratio of the plurality of candidate image blocks to obtain a target area. According to the scheme, the requirement for computing resources is low, the detection speed is high, and the image target detection efficiency and the target detection accuracy are improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image target detection method, device and storage medium. Background technique [0002] With the development of science and technology and the rise of deep learning, the technology of recognizing targets in images has become one of the most important technologies in computer vision, and the application of deep learning in the field of image target detection has made great breakthroughs. A series of Image object learning methods for deep learning algorithms are proposed. For example, deep learning algorithms such as Faster-RCNN (Faster-Regions with Convolutional Neural Networks features) and YOLO (You Only Look Once). Through these deep learning algorithms, the area where an object is located can be identified from a given image, such as objects such as people, cars, or houses are identified on the image. [0003] At present, since the technical focus of imag...

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Problems solved by technology

[0003] At present, since the technical focus of image target detection algorithms based on deep learning such as Faster-RCNN and YOLO is on the accuracy of detection results, the running speed of existing image target detection algorithms does not meet the requirements of actual scenarios. The models of some relatively efficient image object detection systems are very large, which makes the existing image object detection systems run slowly and cannot be implemented on mobile terminals with small computing resource allocation, that is, existing image object detection algorithms are ubiquitous Not only the detection efficiency is low due to the problems of large model and slow speed, but also it is difficult to meet the needs of mobile terminals for real-time detection

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