A small target detection method and a detection model based on a convolutional neural network

A convolutional neural network and small target detection technology, applied in the fields of computer vision and image processing, can solve problems such as poor detection effect of small targets, achieve complex target detection process, reduce redundant extraction, strong practicability and market promotion effect of value

Active Publication Date: 2019-06-14
XIDIAN UNIV
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Problems solved by technology

[0009] The purpose of the present invention is to solve the above problems, provide a small target detection method based on convolutional neural network, strengthen low-level features, reduce redundant extraction

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  • A small target detection method and a detection model based on a convolutional neural network
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  • A small target detection method and a detection model based on a convolutional neural network

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Embodiment Construction

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0057] This embodiment adopts the Caffe deep learning experimental platform, and the specific experimental environment configuration is as follows: the processor of Intel (R) Core (TM) i7-6700, the installation memory of 16G, 64-bit operating system, the operating system is Ubuntu16.04, GPU It is NVIDIA GTX1080Ti with 8G video memory, python version is 2.70, and OpenCV version is 2.4.10. Specifically, it is realized through the following steps: image collectio...

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Abstract

The invention discloses a convolutional neural network-based small target detection method and a convolutional neural network-based small target detection model, and the method comprises the steps: carrying out the marking of small targets in a training set image, and building a small target data set; construction a training platform with caffe-ssd as the bottom layer; constructing a single-step detector model ELFSSD for enhancing low-layer feature fusion; adopting a model through pre-trained VGG-16 model initializing, inputting a small target data set in an lmdb format, carrying out iterativetraining; and detecting a small target in the detection set image by using the trained ELFSSD model. The method enhances the low-level characteristics, removes the high-level redundant characteristics, simplifies the detection process, improves the detection speed, accurately detects the small target in the image in real time, and solves the problem of poor detection effect of the small target inthe prior art.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image processing, and relates to a small target detection method and detection model based on a convolutional neural network, which can be used for small target detection such as industrial rice quality and medical cell analysis under a microscope. Background technique [0002] At present, the rapid development of target detection technology has greatly improved the accuracy and real-time performance of target detection. From the two-step detector RCNN series model to the end-to-end network model SSD, YOLO, etc., the detection effect of the target detection model based on the convolutional neural network in the Kaggle competition and the public data set has been continuously improved, but for small target detection, existing There are obvious problems in the model and the detection effect has not been ideal. [0003] In order to improve the accuracy of the SSD model for small target d...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 布芳贾静姜光李小平刘彦明
Owner XIDIAN UNIV
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