An image target detection method based on DC-SPP-YOLO

A DC-SPP-YOLO, target detection technology, applied in instruments, computing, character and pattern recognition, etc., can solve problems such as restricting target detection accuracy, ignoring local area features, and hindering information flow.

Active Publication Date: 2019-04-26
BEIJING UNIV OF CHEM TECH
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AI Technical Summary

Problems solved by technology

However, the YOLO and YOLOv2 methods still have limitations in the target detection accuracy; when the model learning ability is improved by deepening the network, the gradient disappearance phenomenon will occur. The YOLOv3 algorithm uses the residual connection method to alleviate the gradient disappearance phenomenon but hinders the information

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  • An image target detection method based on DC-SPP-YOLO
  • An image target detection method based on DC-SPP-YOLO
  • An image target detection method based on DC-SPP-YOLO

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Embodiment

[0067] The embodiment uses the public and widely used PASCALVOC (2007+2012) standard data set for image recognition and target detection algorithm performance evaluation to carry out the training and testing of the DC-SPP-YOLO model; wherein the VOC 2007+2012 data set contains image samples 32,487 images, 8,218 images in the training dataset, 8,333 images in the verification dataset, 4,952 images in the VOC 2007 test dataset, and 10,990 images in the VOC 2012 test dataset.

[0068] The computer configuration of embodiment is Intel (R) Xeon (R) E5-2643 3.3GHz CPU, 32.00GB memory, 1 Navida GTX 1080Ti GPU that memory is 11.00GB. The embodiment is carried out on the Windows 10 system Visual Studio 2017 platform, and the deep learning framework used is Darknet, which is realized by programming in C / C++ language.

[0069] Apply the present invention to the above-mentioned PASCAL VOC data set image target detection, the specific steps are as follows:

[0070] Step 1: Use geometric t...

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Abstract

The invention discloses an image target detection method based on DC-SPP-YOLO, which comprises the following steps: firstly, preprocessing a training image sample by using a data enhancement method,constructing a training sample set, aSelecting a prior candidate box for target boundary box prediction by using a k-means clustering algorithm; Then, improving the convolutional layer connection modeof the YOLOv2 model from layer-by-layer connection to dense connection, introducing a space pyramid pooling between the convolutional module and a target detection layer, and establishing DC-SPP-YOLOtarget detection model; And finally, constructing a loss function by using an error quadratic sum between the predicted value and the real value, and iteratively updating model weight parameters to converge the loss function to obtain the DC-SPP-YOLO model for target detection. The invention considers the gradient disappearance caused by deepening convolution network and the insufficient use of multi-scale local region features of YOLOv2 model, and constructs a DC-SPP-YOLO target detection model based on improved convolution layer dense connection and spatial pyramid pooling. the target detection accuracy is improved.

Description

technical field [0001] The present invention relates to an image target detection method, which belongs to the technical field of machine vision, and in particular to a target detection method based on dense connection and spatial pyramid pooling YOLO (Dense Connectivity and Spatial Pyramid Pooling Improved You Look Only Once, DC-SPP-YOLO) . Background technique [0002] Object detection is one of the core research contents in the field of machine vision, and it is widely used in driving navigation, workpiece detection, robotic arm grasping, etc. Establish and train a high-quality target detection model, which can extract more abundant and effective target features, and improve the accuracy of locating and classifying targets in images or videos. [0003] Traditional target detection methods such as Deformable Parts Models (DPM) search for target positions through sliding windows, which is inefficient; extracting artificially designed features such as histogram of oriented ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V2201/07G06F18/23213G06F18/214G06F18/2415
Inventor 王建林黄展超邱科鹏
Owner BEIJING UNIV OF CHEM TECH
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