Small target detecting method based on R-FCN

A small target detection and target frame technology, applied in the field of image processing, can solve the problems of insufficient detection accuracy and easy occurrence of false negatives, so as to reduce the false negative rate, reduce false negatives, and improve detection accuracy.

Active Publication Date: 2017-09-08
JIANGNAN UNIV
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Problems solved by technology

[0003] The deep convolutional neural network continuously learns and adjusts on the basis of a large number of training samples, which makes it perform well in target detection tasks, but it is mainly used for the detection of s

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  • Small target detecting method based on R-FCN

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Embodiment Construction

[0035] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0036] The invention discloses a small target detection method based on R-FCN, wherein the small target to be detected is a target whose area in the image to be tested is smaller than a predetermined area, and the predetermined area is a user or system preset value, for example, a small target can be Pedestrians, flames and smoke, etc. In the present invention, the small target is a pedestrian as an example, and the images to be tested are in the form of pictures and videos. R-FCN Region-based Fully Convolutional Networks in the present invention, region-based fully convolutional network) includes convolutional network, RPN (Region Proposal Networks, candidate window network) and classifier, the basic model of convolutional network can be ResNet Residual network, the basic model of the convolutional network used in the present invention is R...

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Abstract

The invention discloses a small target detecting method based on R-FCN, wherein the method relates to the field of image processing. The method comprises the steps of introducing a to-be-detected image into a convolutional network, successively performing characteristic extraction on a to-be-detected image through M network layers according to a sequence from a topmost layer of M network layers to a downmost layer and according to a sequence from the downmost layer of the M network layers to the topmost layer, generating characteristic mapping graphs with different scales, selecting an N characteristic mapping graphs into an RPN for performing foreground-and-background classification, determining the coordinate of a foreground area, processing a characteristic mapping block which corresponds with the coordinate of the foreground area for obtaining a characteristic vector; inputting each characteristic vector into a classifier for performing secondary classification, detecting whether the kind to which the characteristic vector is affiliated corresponds with a to-be-detected small target and outputting a detecting result. According to the small target detecting method, a manner of combining a top-down characteristic pyramid and a down-top characteristic pyramid is utilized for performing small target detection on the characteristic mapping graphs with different scales, thereby reducing report omission for the small target and improving detecting precision.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an R-FCN-based small target detection method. Background technique [0002] Pedestrian detection has a wide range of applications in areas such as intelligent assisted driving, intelligent monitoring, pedestrian analysis, and intelligent robots. Therefore, how to optimize the performance and speed of pedestrian detection has become an important topic. The more commonly used method of pedestrian detection is to detect pedestrians in the image to be tested by collecting image information such as infrared information and hyperspectral information of the image to be tested. This method does not directly use the RGB image of the image to be tested. Information requires specific instruments to collect infrared information and hyperspectral information, so the detection is not direct enough, the accuracy is not high, and the detection speed is not fast. With the development of deep conv...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/241G06F18/214
Inventor 黄敏蒋胜朱启兵
Owner JIANGNAN UNIV
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