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Image target detection method and system

A target detection and image technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problems of algorithm AP (low accuracy rate, low object detection accuracy rate of small targets, etc.), and achieve the effect of improving performance

Pending Publication Date: 2022-05-20
FUJIAN YIRONG INFORMATION TECH +2
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, although there are some very mature algorithms for image target detection, some algorithms have low AP (accuracy rate), especially for small target object detection accuracy.

Method used

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  • Image target detection method and system
  • Image target detection method and system
  • Image target detection method and system

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Experimental program
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Effect test

Embodiment 1

[0047] This embodiment provides a method for image target detection, such as figure 1 shown, including:

[0048] Obtain the image to be detected, input the image to be detected into the backbone network module and the spatial network module to extract features respectively, and the backbone network module inputs the image feature data extracted by multiple convolution layers into the spatial network module and the fusion network module respectively;

[0049] The spatial network module obtains high-resolution spatial features through the input image to be detected and the image feature data of multiple convolutional layers input by the backbone network module, and adds a spatial attention mechanism after each convolutional layer to strengthen spatial information , and finally integrate multiple spatial information of different scales and output;

[0050] The fusion network module combines the image feature data of multiple convolutional layers input by the backbone network mod...

specific Embodiment approach

[0053] The image feature data extracted by multiple convolutional layers of the backbone network module are respectively input to the spatial attention mechanism SAM of the space network module through the corresponding multi-level attention module MAM, and the multi-level attention module MAM is used to obtain multiple Level feature information, and then combine the multi-level feature information to achieve feature enhancement.

[0054] The multi-level attention module MAM is specifically used to use F n Get F after upsampling n-1 , combining two levels of features F n and F n-1 (F n Indicates the feature after the nth layer of convolution, F n-1 express to F n The features after upsampling) are concatenated to obtain the combined feature F, and then the feature F is transformed by a 3×3 convolution with batch regularization and nonlinear units to obtain the feature F'. The multi-level attention module MAM has Two hyperparameters: expansion rate d and compression rate ...

Embodiment 2

[0087] In this embodiment, an image target detection system is provided, such as Figure 6 As shown, including: backbone network module, space network module and fusion network module;

[0088] The backbone network module and the spatial network module are respectively connected to the input, and the plurality of convolution layers in the backbone network module are respectively connected to the spatial network module and the fusion network module, and the output of the backbone network module is passed through the convolution module Connected to the detection and identification module, the output ends of the space network module and the fusion network module are respectively connected to the detection and identification module, and the detection and identification module is connected to the filtering module.

[0089] A specific implementation of the image target detection system, the backbone network module includes: a first convolutional layer, a second convolutional layer, ...

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Abstract

The invention discloses an image target detection method and system, and the method comprises the steps: employing a network structure based on an attention mechanism to extract image features, employing a region generation network to generate a candidate region, employing non-maximum suppression to remove redundant candidate boxes, and obtaining a final detection result. The target detection process comprises the following steps: inputting a picture, and extracting features of the picture through a backbone network and a spatial network; and the backbone network inputs the image feature data after convolution to the spatial network module and the fusion network module. The output features of the spatial network module, the backbone network module and the fusion network module and the output features of the subsequently added convolutional layers are used as the basis of subsequent regression and classification. And generating a plurality of prior frames through an RPN algorithm, and filtering through an NMS to obtain a final target detection result. According to the embodiment of the invention, through the image target detection method based on the attention mechanism, the detection and recognition capability of the small target object is further improved under the condition of improving the overall recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of image target detection, in particular to an image target detection method and system. Background technique [0002] Image target detection, also called target extraction, is an image segmentation based on target geometric and statistical features. It combines target segmentation and recognition into one, and its accuracy and real-time performance are an important capability of the whole system. Especially in complex scenes, when multiple targets need to be processed in real time, automatic target extraction and recognition is particularly important. [0003] From the perspective of the past ten years, the target detection algorithm of natural images can be roughly divided into the period based on traditional manual features, and the period of target detection based on deep learning. In terms of technical development, the development of target detection has experienced "bounding box regression", "the ris...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06T7/11G06V10/80G06V10/82
CPCG06T7/11G06T2207/20084G06T2207/20016G06N3/045G06F18/253G06F18/24
Inventor 陈江海苏江文卢伟龙
Owner FUJIAN YIRONG INFORMATION TECH
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