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Deep learning target detection method and system

A target detection and deep learning technology, applied in the field of computer vision, can solve the problem that the target function of the detection network cannot be optimized, and achieve the effect of accurate target detection results and fast training

Active Publication Date: 2020-05-15
重庆特斯联智慧科技股份有限公司
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Problems solved by technology

[0003]However, in the prior art, when the predicted frame and the real frame do not overlap, the objective function of the detection network cannot be optimized

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  • Deep learning target detection method and system
  • Deep learning target detection method and system
  • Deep learning target detection method and system

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[0036] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0037] At present, the mainstream bounding box optimization of target detection uses the bounding box regression loss, such as using the square difference (MSE) loss or smooth L1 (smooth-L1) loss, etc. These loss calculation methods use the distance measure of the bounding box, and ignore The most notable property of the bounding box itself: the intersection-over-union ratio (IoU), that is, the ratio of the intersection and union of...

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Abstract

The invention discloses a deep learning target detection method. The method comprises the following steps: inputting a picture, and extracting features of a target in the picture through a convolutional neural network backbone network; firstly, generating a plurality of sliding windows with different scales and length-width ratios through a regional candidate network, and then obtaining candidateboxes possibly containing targets through classification and frame regression operation; mapping to a feature layer of a target to be detected through the candidate box, and performing pooling operation on an interested target area to obtain a pooled feature map with the same dimension of the target area; respectively performing classification and frame regression operations on the feature layer of the target to be detected; and obtaining a final target detection box after non-maximum suppression. The method has the advantages that the problem that the target function of the detection networkcannot be optimized when the prediction frame and the real frame are not overlapped is avoided; a Newton method can be used to update network weight, and detection network training is faster. High target detection result accuracy is realized.

Description

technical field [0001] The invention belongs to the field of computer vision based on deep learning, and in particular relates to a deep learning target detection method and system. Background technique [0002] With the rise of deep convolutional neural networks, it has become a better tool to handle object detection tasks. For example, in the process of taking pictures on mobile phones, in the pre-shooting mode, the predicted box (usually a rectangle or square framing the face) is displayed, and the detection network usually effectively learns to mark a pixel as background or foreground. The loss function is a measure that quantifies the error between the predicted value and the true value. Typically, cross-entropy loss is used as a loss function of choice in pixel labeling tasks. This loss function is used in segmentation or classification tasks, satisfying the condition as long as the labels of all classes are balanced. The class imbalance makes the network prediction...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V2201/07G06N3/045G06F18/2415
Inventor 贾琳赵磊
Owner 重庆特斯联智慧科技股份有限公司
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