Target detection method based on improved Mixed Pooling-YOLOV3

A target detection and target technology, which is applied in the target detection field of MixedPooling-YOLOV3, can solve the problems of overfitting, unbalanced positive and negative samples, low precision, etc., and achieve the effects of improving accuracy, reducing gradient disappearance, and fast convergence

Pending Publication Date: 2020-06-30
TIANJIN CHENGJIAN UNIV
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to integrate the ideas of "hybrid pooling" and "reconstruction loss function" into the network structure of YOLOV3 in view of the problems of low precision,

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  • Target detection method based on improved Mixed Pooling-YOLOV3
  • Target detection method based on improved Mixed Pooling-YOLOV3
  • Target detection method based on improved Mixed Pooling-YOLOV3

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Embodiment Construction

[0034] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0035] Such as Figure 4 As shown, the improved Mixed Pooling-YOLOV3 target detection method based on the embodiment of the present invention includes the following steps:

[0036] a. Create image datasets in unnatural scenes, and perform preprocessing operations on some images;

[0037] b. After the data preprocessing is completed, optimize the DPM network parameters and start model training according to the target type to be identified;

[0038] c. After training the model, input the collected images into the model for testing to realize target recognition and positioning.

[0039] In the aforementioned step a, the format of the image data set is VOC format; the data ...

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Abstract

The invention discloses a target detection method based on improved Mixed Pooling-YOLOV3, and the method carries out the design of a target detection algorithm based on the idea of regression, and achieves the multi-scale and multi-label classification. Based on the defects of a current one-stage target detection method YOLOV3, a DPM network based on a Darknet-53 structure is designed as a featureextractor; and secondly, an original loss function is reconstructed, and meanwhile, parameters of the original loss function are optimized, so the detection precision is effectively improved, and convergence is quicker. The DMP framework gives consideration to the complexity of the network and the accuracy of detection; compared with a common target detection feature extraction network VGG-16, the method has the advantages that the model calculation amount is reduced, the latest progress of computer vision is introduced into the field of target detection, the detection precision and the detection speed are good, and meanwhile, the method has better performance and popularization and application prospects.

Description

technical field [0001] The present invention relates to the technical fields of computer vision, deep learning technology and target detection, in particular to a target detection method based on improved Mixed Pooling-YOLOV3. Background technique [0002] As one of the most fundamental and challenging problems in computer vision, object detection has received tremendous attention in recent years. Object detection is a basic computer vision task, which provides basic information for semantic segmentation of image and video understanding, and can also be used to detect instances of specific categories of visual objects in digital images, so it has received extensive attention. The goal of object detection is to develop computational models and techniques that provide the basic information needed for computer vision applications: what is the object, and where is it? From the perspective of application, target detection can be divided into two research topics: "general object ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241Y02T10/40
Inventor 郝琨郭飞赵璐
Owner TIANJIN CHENGJIAN UNIV
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