Multi-target detection method, device and electronic equipment

Abstract

The present application proposes a multi-target detection method, device, and electronic equipment, wherein the multi-target detection method includes: generating multiple detection frames according to the target; obtaining classification probabilities of the multiple detection frames; The classification probability generates the confidence of the multiple detection frames respectively, and acquires the stability of the multiple detection frames; and screens the multiple detection frames according to the confidence and stability of the multiple detection frames. This method can effectively utilize computing resources of electronic devices, and detect meaningful targets in real time and stably through the detection frame, thereby reducing resource occupation on the server side, reducing user traffic consumption, and improving user experience in related application scenarios.

Description

technical field [0001] The present application relates to the technical field of electronic equipment, in particular to a multi-target detection method, device and electronic equipment. Background technique [0002] At present, in order to ensure the accuracy of target detection, by deploying complex target detection models on the server side, when users use electronic devices to take pictures or videos, they can remotely access the detection service on the server side to obtain detection results. The detection results include Each object included in the picture or video frame is taken. [0003] However, in actual application, the applicant found that the above method of obtaining test results has the following technical problems: [0004] First, since electronic devices need to continuously upload images to the server side to obtain detection results, the acquisition of detection results greatly depends on the real-time nature of the network. In the case of network failure...

AI Technical Summary

Problems solved by technology

[0004] First, since electronic devices need to continuously upload images to the server side to obtain test results, the acquisition of test results greatly depends on the real-time performance of the network. There is obvious lag and consumes a lot of network traffic for users;

[0005] Second, increase the resource occupation on the server side. In scenarios such as users shooting videos, the server side needs to continuously detect video frames, which brings greater service request pressure on the server side.

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