Vehicle detection method based on multi-branch cyclic self-attention network and cyclic frame regression

A vehicle detection and attention technology, which is applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of inaccurate detection frames, inaccurate features, and low detection accuracy of occluded vehicles.

Active Publication Date: 2019-07-09
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

In addition, traditional deep learning algorithms only select features once or select features twice as features for subsequent fine-tuning.
The above two shortcomings lead to the fact that the features selected by the current vehicle detection algorithm are still not accurate enough, the predicted detection frame is no

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  • Vehicle detection method based on multi-branch cyclic self-attention network and cyclic frame regression
  • Vehicle detection method based on multi-branch cyclic self-attention network and cyclic frame regression
  • Vehicle detection method based on multi-branch cyclic self-attention network and cyclic frame regression

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[0046] Example

[0047] This embodiment discloses a vehicle detection method based on multi-branch cyclic self-attention network and cyclic border regression, which specifically includes the following steps:

[0048] Step S1, use the convolutional layer, BN layer, Relu layer, and pooling layer to construct a backbone network for vehicle detection as a feature extractor for images. The specific structure of the backbone network is as follows:

[0049] The connections from the input layer to the output layer are: convolutional layer conv1_1, BN layer conv1_1_bn, Relu layer conv1_1_relu, convolutional layer conv1_2, BN layer conv1_2_bn, Relu layer conv1_2_relu, pooling layer max_pooling1, convolutional layer conv2_1, BN layer conv2_1_bn, Relu layer conv2_1_relu, convolutional layer conv2_2, BN layer conv2_2_bn, Relu layer conv2_2_relu, pooling layer max_pooling2, convolutional layer conv3_1, BN layer conv3_1_bn, Relu layer conv3_1_relu, convolutional layer conv3_2, BN layer conv3...

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Abstract

The invention discloses a vehicle detection method based on a multi-branch cyclic self-attention network and cyclic frame regression. The vehicle detection method comprises the following steps: constructing a trunk network for vehicle detection; predicting a candidate box by using an RPN network, extracting an instance feature map according to the candidate box, circularly predicting the self-attention map and calculating a new instance feature map to obtain a final instance feature map; circularly selecting a feature map output by the basic network according to the predicted detection frame by using circular frame regression; and expanding the computing network to multiple branches by using a multi-branch network structure, and fusing a multi-branch detection result. Compared with a traditional vehicle detection method based on deep learning, the vehicle detection method based on the multi-branch cyclic self-attention network and the cyclic frame regression has the advantages that more accurate features of the vehicle can be obtained, the confidence coefficient of a detection frame of the vehicle is improved, a more accurate detection frame can be obtained, and the detection accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of vehicle detection, in particular to a vehicle detection method based on a multi-branch cyclic self-attention network and cyclic frame regression. Background technique [0002] Vehicle detection is an important part of driver assistance system (ADAS) and automatic driving system (ADS). A vehicle detection algorithm with higher accuracy is of great significance to the safety of automatic driving systems and assisted driving systems. Due to the strong generalization ability and fitting ability of deep learning, the vehicle detection algorithm based on deep learning has greatly improved in terms of accuracy. At present, the vehicle detection algorithms based on deep learning mainly include Fast RCNN, Faster RCNN, SSD, etc. In the target detection algorithm based on deep learning, in the training phase, the input image is input to the convolutional neural network to extract features, and the matching algorit...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/584G06F18/241G06F18/214
Inventor 周智恒黄宇
Owner SOUTH CHINA UNIV OF TECH
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