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Target semantic segmentation method and street target anomaly detection method using same

A semantic segmentation and target technology, applied in the field of target detection, can solve the problems of difficult segmentation of small targets, inaccurate segmentation of large targets, and difficulty in labeling data.

Active Publication Date: 2022-01-28
CITY CLOUD TECH HANGZHOU CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The current semantic segmentation obviously has problems such as difficulty in labeling data, difficulty in segmenting small objects, and inaccurate segmentation of large objects.

Method used

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  • Target semantic segmentation method and street target anomaly detection method using same
  • Target semantic segmentation method and street target anomaly detection method using same
  • Target semantic segmentation method and street target anomaly detection method using same

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] This embodiment provides a target semantic segmentation method for performing target semantic segmentation on an image to be detected. like figure 1 As shown, the method can be summarized into 3 steps:

[0052] Step 1, use the region growing algorithm to segment the target in the image to be detected, and obtain the segmentation contour of each target in the image to be detected; Step 2, input the image to be detected into the segmentation network to obtain the target category probability of each pixel, according to each The target category probability of one pixel is used to obtain the predicted contour of each target; step 3, according to the segmentation contour and predicted contour corresponding to the same target, the semantic segmentation result of the target is obtained.

[0053] In step 1, first obtain the image to be detected, input the image to be detected into the target detection network for target detection, obtain at least one detection frame, then deter...

Embodiment 2

[0086] Based on the same idea, this embodiment also provides a target semantic segmentation device for implementing the target semantic segmentation method described in Embodiment 1. The device includes the following modules:

[0087] a first acquiring module, configured to acquire an image to be detected including at least one target;

[0088] A target detection module, configured to input the image to be detected into a target detection network to obtain at least one detection frame of the target;

[0089] A region growing module, configured to determine at least one growth region according to each of the detection frames, select an initial seed point in each of the growth regions to perform region growth, and obtain a corresponding The segmentation contour of the target;

[0090] The first segmentation module is used to input the image to be detected into the target segmentation network to obtain the target category probability of each pixel, and determine each pixel whose...

Embodiment 3

[0097] This embodiment also provides an electronic device, refer to Figure 4 , including a memory 404 and a processor 402, wherein a computer program is stored in the memory 404, and the processor 402 is configured to run the computer program to perform the steps of any one of the object semantic segmentation method or street object anomaly detection method in the above-mentioned embodiments .

[0098] Specifically, the processor 402 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

[0099]Wherein, the memory 404 may include a mass memory 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (Hard Disk Drive, referred to as HDD), a floppy disk drive, a solid state drive (Solid State Drive, referred to as SSD), flash memory, optical disk, magneto-o...

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Abstract

The invention provides a target semantic segmentation method. The method comprises the following steps: acquiring a to-be-detected image comprising at least one target; inputting the to-be-detected image into a target detection network to obtain a detection frame of at least one target; setting a growth region and initial seed points according to the detection frame, and obtaining a segmentation contour of a corresponding target according to a region growth result of each initial seed point; inputting the to-be-detected image into the predicted contour of each target of the target segmentation network; and obtaining a semantic segmentation result of the corresponding target according to the union set region of the segmentation contour and the prediction contour of each same target. According to the method, the problem of difficulty in labeling in semantic segmentation is solved by generating a detection frame of a target through target detection, a growth region and an initial seed point are determined according to the detection frame, a more accurate segmentation contour of the target can be obtained by segmenting the target through a region growth algorithm, a prediction contour of the target is obtained through a target segmentation network, and a semantic segmentation result is obtained according to the segmentation contours of the same target.

Description

technical field [0001] The present application relates to the technical field of target detection, in particular to a target semantic segmentation method and a street target anomaly detection method using the same. Background technique [0002] Semantic segmentation is to classify each pixel in an image, which is currently widely used in various fields. The usual semantic segmentation architecture can be broadly thought of as an encoder network and a decoder network: the encoder is usually a pre-trained classification network such as vgg / resnet, and the task of the decoder is to integrate the discriminative features learned by the encoder into (low-resolution) semantic projection onto pixel space (high-resolution), resulting in per-pixel classifications. [0003] The current semantic segmentation obviously has problems such as difficulty in labeling data, difficulty in segmenting small objects, and inaccurate segmentation of large objects. Contents of the invention [00...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/00G06K9/62G06T7/90G06V10/764G06V10/774G06V10/80G06V10/74
CPCG06T7/11G06T7/0002G06T7/90G06F18/22G06F18/214G06F18/2415G06F18/253
Inventor 徐志坚章东平陈斌雷羽文董墨江
Owner CITY CLOUD TECH HANGZHOU CO LTD