Dense connection based deep learning object detection method and device

Abstract

The invention provides a dense connection based deep learning object detection method and device, belongs to the technical field of image detection, and aims at solving the problem that a smaller object in an image is hard to detect accurately in the prior art. According to the method, a pre-constructed object detection network model is used to detect objects in an input image, and an object classification result and coordinate position in the input image are obtained. Thus, multidimensional characteristics can be extracted from the input image, and the small object in the image can be described in a better way. The device can be used to execute the method.

Description

technical field [0001] The invention belongs to the technical field of image detection, and in particular relates to a dense connection-based deep learning object detection method and device. Background technique [0002] With the development of technologies such as neural networks, computer vision, artificial intelligence, and machine perception, object detection, as an important part of the above technologies, has also made great progress. Object detection refers to the use of computers to analyze images and obtain objects in images. location and category information. Traditional object detection methods rely on artificially designed features to identify the position information and category information of objects in images, but artificially designed features are easily disturbed by light changes, object color changes, and background noise, resulting in poor robustness in practical applications. , it is difficult to meet the accuracy requirements of users. [0003] With ...

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Problems solved by technology

[0004] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem that the prior art is difficult to accurately detect smaller objects in the image, the present invention provides a dense connection-based deep learning object detection method, including:

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