A method for detecting floating objects on water based on YOLOv2 network

A technology of floating objects and floating objects on water, applied in the fields of computer vision and machine learning, can solve problems such as water pollution, achieve the effects of improving accuracy, eliminating detection interference, and preventing overfitting problems

Inactive Publication Date: 2018-12-18
JIANGNAN UNIV
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And judge the pollution degree of the river or lake according to the number

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  • A method for detecting floating objects on water based on YOLOv2 network
  • A method for detecting floating objects on water based on YOLOv2 network
  • A method for detecting floating objects on water based on YOLOv2 network

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[0030] The invention realizes the detection method of floating objects on the water based on the YOLOv2 network, which mainly includes five parts: data collection, data enhancement, marking pictures, training module and detection module.

[0031] In order to better understand the detection method of floating objects based on the YOLOv2 network, first explain the working principle of the YOLOv2 network, such as figure 2 Shown:

[0032] The YOLOv2 network structure contains 19 convolutional layers and 5 maximum pooling layers. The network introduces the idea of ​​anchor box in Faster RCNN, and improves the design of the network structure. The output layer uses a convolutional layer to replace the fully connected layer of YOLO, and uses coco object detection and annotation data and imagenet object classification and annotation data to train object detection. model.

[0033] Different from the RCNN series of methods, RCNN needs to generate suggestion boxes, and perform classification ...

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Abstract

The invention relates to the field of computer vision and machine learning, in particular to a method for detecting floating objects on water based on a YOLOv2 network. The method comprises the stepsof: 1, collecting data: 2, data enhancement: performing data enhancement of the data set A; 3, marking the picture: marking the floating object area on the water in the data set B with a rectangular frame, and 4, training the module: the purpose of dividing dataset B into three parts is to select the best weight model with the best generalization ability; 5, detecting the module: the trained weight model is used to detect the monitored river or lake video. The invention has the beneficial effect of replacing the traditional detection method based on artificiality, saving manpower and materialresources, and judging the pollution degree of a river or a lake. The invention randomly divides the data set into a training set, a test set and a verification set, and expands the sample through a data enhancement method to prevent the over-fitting problem caused by too few image samples.

Description

technical field [0001] The invention relates to the fields of computer vision and machine learning, in particular to a method for detecting floating objects on water based on a YOLOv2 network. Background technique [0002] my country's fresh water resources continue to decrease, and pollution is more serious. A large number of floating objects appear on the surface of lakes, rivers and other waters, and these floating objects contain a large amount of substances harmful to human body. Therefore, for the sustainable development of human beings, the problem of water pollution must be solved. Although cameras are currently used to monitor the water surface in some scenarios, someone still needs to watch the monitoring screen, which is not only time-consuming and laborious, but also cannot guarantee accurate and real-time response to floating objects. [0003] In order to meet the needs of practical applications, in view of the various deficiencies in the current detection of ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/20G06F18/214
Inventor 肖志勇刘辰
Owner JIANGNAN UNIV
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