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Improved target detection method based on RFCN algorithm

A target detection and algorithm technology, applied in computing, computer parts, instruments, etc., can solve the problem of not using the proposed network, and achieve the effect of enhanced positioning capability, enhanced positioning capability, and increased accuracy.

Pending Publication Date: 2020-04-24
TIANJIN UNIV
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Problems solved by technology

For example: Couple Net (composite network) is a detection method that combines the global information of the target proposal on the basis of RFCN, and Cascade Net only achieves higher detection networks by cascading multiple detection networks with different IOU (overlap) thresholds. At the same time, they only use RPN (Regional Proposal Network) when making regional proposals, instead of using a better proposal network

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Embodiment Construction

[0040] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0041] This method provides an improved target detection method based on the RFCN algorithm, involving convolutional layers, regions of interest, proposed network-AttractioNet, location-sensitive score maps, and prediction network components. The functions of each module are as follows:

[0042]Convolutional layer, this module is to extract the feature information in the image through a series of convolution operations, usually the convolutional layer includes 3*3, 1*3, 1*1 or 7*7 convolution kernels, for different The size and number of convolution kernels used in the network structure are also different. In this method, the ResNet-101 network is used for feature extraction, where t...

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Abstract

The invention discloses an improved target detection method based on an RFCN algorithm. The method comprises the following steps: firstly, a proposal network-AttractioNet with better performance is adopted when regional proposal is carried out; the candidate box can better frame the target, then two prediction networks are additionally cascaded in the original network prediction stage, and the prediction threshold of each stage is gradually increased, so that the accuracy of target detection is further improved.

Description

technical field [0001] The invention belongs to the field of target detection and image processing, in particular to an improved target detection method based on RFCN algorithm. Background technique [0002] In recent years, with the rise of the concept of deep learning, technology in the field of computer vision has flourished. Although there are many methods for researching target detection, they can be roughly divided into two mainstreams: one is the target detection method based on region proposal, such as the RCNN series (RCNN, Fast RCNN and RFCN, etc.); the other is the single-stage target detection method. , such as YOLO, SSD and DSOD, etc. [0003] The main idea of ​​the single-stage detection method is to uniformly perform dense sampling at different positions of the picture. Different scales and aspect ratios can be used for sampling, and then use CNN (convolutional neural network) to extract features and directly perform classification and regression. The proces...

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Application Information

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IPC IPC(8): G06K9/32G06K9/46G06N3/04
CPCG06V10/25G06V10/44G06V2201/07G06N3/045
Inventor 陈景明金杰李燊郭如意
Owner TIANJIN UNIV
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