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Target detection and recognition method based on deep learning

A deep learning and pedestrian detection technology, applied in the field of target detection and recognition based on deep learning

Active Publication Date: 2017-10-03
NORTHEASTERN UNIV
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Problems solved by technology

But how to use deep learning to complete the target detection task is still in the preliminary stage of research

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  • Target detection and recognition method based on deep learning
  • Target detection and recognition method based on deep learning
  • Target detection and recognition method based on deep learning

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation examples.

[0033] see figure 2 , (1) The beginning of the model is alternately connected by 5 convolutional layers and pooling layers. The main purpose is to propose the features of the image through the convolutional layer, and reduce the dimension of the image through the pooling layer to reduce the calculation dimension accordingly.

[0034] (2) Next is the RPN layer. This method uses two RPN layers to generate candidate windows. The input of one RPN layer is from Conv-5 and the input of the other convolutional layer is from Conv-3. Screening is done based on the windows generated on these two layers. According to the proportional relationship between the feature map sizes in the two convolutional layers, the feature window coordinate position is mapped to the same scale. If the position is repeatedly detected, the target confidence is re...

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Abstract

The invention discloses a target detection and recognition method based on deep learning. A model is built by utilizing a caffe platform in a Linux system, and consists of convolution layers, pooling layers, RPN layers, an ROIs layer, an ROI Pooling layer, fully-connected layers, a Sparse PCA layer and prediction window and prediction category output layers. A multi-RPN-layer fusion strategy is provided by the invention, and the detection capacity for a target of each scale is enhanced; the new Spares PCA layer is added between the two fully-connected layers, and the accuracy is guaranteed while the computational amount is reduced; and lastly, a logarithmic suppression method is provided for target location prediction, and a target location regression algorithm is improved. Finally, the purpose that the detection speed is accelerated while the detection precision is guaranteed is achieved. The method realizes accurately and quickly detecting and recognizing the targets of interest, and has a great application value.

Description

technical field [0001] The invention belongs to the technical field of computer vision recognition, and relates to a target detection and recognition method based on deep learning. Background technique [0002] In the past five years, with the breakthrough of the new intelligent computing method—the theoretical basis of deep learning, various technologies of artificial intelligence, such as speech recognition technology, image recognition technology, data mining technology, etc., have achieved substantial development and have been successfully applied in in multiple products. As a pivotal science and technology in the field of artificial intelligence, computer vision has received extensive attention from academia and industry. In particular, convolutional neural networks have achieved very good results in the field of image target detection and recognition. [0003] From the analysis of recent academic research, the traditional target detection algorithm has reached the bo...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V20/10G06V2201/07G06N3/045
Inventor 张云洲付兴张鹏飞李奇贾存迪郑瑞刘双伟
Owner NORTHEASTERN UNIV
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