Multi-target detection method based on a cascade hourglass neural network

A neural network and detection method technology, applied in the field of multi-target detection, can solve problems such as poor effect and slow speed, and achieve the effect of improving detection accuracy and fast operation speed

Active Publication Date: 2019-05-17
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a multi-target detection method based on cascaded hourglas

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  • Multi-target detection method based on a cascade hourglass neural network
  • Multi-target detection method based on a cascade hourglass neural network
  • Multi-target detection method based on a cascade hourglass neural network

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Embodiment Construction

[0021] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings. However, the following examples are only used to illustrate the present invention in detail, and do not limit the scope of the present invention in any way.

[0022] Such as Figure 4 As shown, a kind of multi-target detection method based on cascaded hourglass neural network of the present invention comprises the following steps:

[0023] Step 1, such as figure 1 As shown, collect training samples: use the image acquisition equipment to detect the target image, mark the target image and do preprocessing to make the image meet the format requirements, and build a training sample set; image preprocessing uses image enhancement, including random angle ( -180°, 180°) rotation and random size scaling (0.5x-2x).

[0024] Step 2. Build a deep learning framework and build a backbone network cascaded hourglass network for target detect...

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Abstract

The invention provides a multi-target detection method based on a cascaded hourglass neural network, and aims to solve the technical problems that an existing detection method is too slow in speed anda small target is difficult to identify. The method comprises the following steps: step 1, collecting a training sample; step 2, building a deep learning framework and a backbone network cascade hourglass network of target detection; step 3, designing a label of the training sample as a confidence coefficient heat map; step 4, designing a loss function of the cascade hourglass network to optimizethe loss function; step 5, training the cascaded hourglass network to obtain a detection model; And step 6, multi-target detection. The method has the beneficial effects that various types of targetscan be quickly and accurately identified, and the small target identification capability is improved.

Description

technical field [0001] The invention relates to the technical field of multi-target detection, in particular to a multi-target detection method based on a cascaded hourglass neural network. Background technique [0002] Multi-target detection is an important direction in the field of computer vision. The main task is to locate the target of interest from the image and judge the specific category of each target. It has been widely used in medical target detection, intelligent video surveillance, vehicle automatic driving, pedestrian detection, traffic flow statistics, etc. Traditional methods use directional gradient histograms, local binary pattern features, etc. to extract image features, and then use support vector machines, random forests, and neural networks to classify. But it has two disadvantages of slow speed and low precision. [0003] Convolutional neural networks have achieved large-scale success in images in recent years. Girshick proposed that RCNN and Fast-R...

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06N3/04
Inventor 胡钦涛段倩文毛耀刘琼吴水琴周翕
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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