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Multi-scale target detection method based on deep convolutional neural network

A neural network and deep convolution technology, applied in the field of computer image processing, can solve the problems of low accuracy of small-scale objects, reduced robustness and effectiveness, loss of small-scale object features, etc.

Active Publication Date: 2018-09-21
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The deeper the neural network is, the more representative the feature information it expresses, but the problem is that it is very rough for small-scale objects, and even some features of small-scale objects will be lost. The size scale is very sensitive, and there are great differences in the feature information extracted by the neural network for objects of different sizes and scales, resulting in low accuracy of small-scale object detection, which greatly reduces the robustness and effectiveness of target detection.

Method used

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with specific examples.

[0069] like figure 1 As shown, the multi-scale target detection method based on deep convolutional neural network provided in this embodiment, its specific situation is as follows:

[0070] Step 1. Obtain the highway video dataset, then obtain its video frames, manually mark them, and divide them into training datasets and verification datasets.

[0071] Step 2, converting the image and label data of the image dataset into the format required for training the deep convolutional neural network through preprocessing, including the following steps:

[0072] In step 2.1, the image in the dataset is scaled to a size of 768×1344 pixels in length and width, and the label data is also scaled to the corresponding size according to the corresponding ratio.

[0073] In step 2.2, in the scaled image, randomly crop the place containing the label to obtain a square image with a size of 76...

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Abstract

The invention discloses a multi-scale target detection method based on a deep convolutional neural network. The method comprises the steps of (1) data acquisition; (2) data processing; (3) model construction; (4) loss function definition; (5) model training; and (6) model verification. The method combines the ability of extracting image high-level semantic information of the deep convolutional neural network, the ability of generating candidate regions of region generation networks, the repair and mapping abilities of a content-aware region-of-interest pooling layer and the precise classification ability of multi-task classification networks, and therefore multi-scale target detection is completed more accurately and efficiently.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a multi-scale target detection method based on a deep convolutional neural network. Background technique [0002] Object detection and recognition is one of the important topics in the field of computer vision computing. With the development of human science and technology, the important technology of target detection is constantly being fully utilized. People apply it to various scenarios to achieve various expected goals, such as battlefield alert, safety detection, traffic control, video surveillance, etc. aspect. [0003] In recent years, with the rapid development of deep learning, deep convolutional neural networks have made further breakthroughs in target detection and recognition technology. Using the deep convolutional neural network, the high-level semantic feature information of the picture can be extracted, and then these high-level semantic inform...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/48G06N3/04
CPCG06V10/46G06N3/045
Inventor 徐雪妙肖永杰胡枭玮
Owner SOUTH CHINA UNIV OF TECH
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