Small target detection-oriented network and difficult sample mining method

A small target detection and network technology, applied in the field of small target detection, can solve problems such as difficulty in directly processing pictures, and achieve the effect of increasing computational overhead and improving detection accuracy

Pending Publication Date: 2020-03-27
SOUTH CHINA UNIV OF TECH
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Limited by GPU memory, it is difficult for existing dee

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  • Small target detection-oriented network and difficult sample mining method
  • Small target detection-oriented network and difficult sample mining method
  • Small target detection-oriented network and difficult sample mining method

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[0052] The following description with reference to the accompanying drawings is provided to help a comprehensive understanding of various embodiments of the present disclosure defined by the claims and their equivalents. It includes various specific details to help understanding, but these are considered to be merely exemplary. Therefore, those of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described in the present invention without departing from the scope and spirit of the present disclosure. In addition, for clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0053] The terms and words used in the following description and claims are not limited to the written meaning, but are only used by the inventor to enable a clear and consistent understanding of the present disclosure. Therefore, it should be clear to those skilled in the art that the following descripti...

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Abstract

The invention discloses a small target detection-oriented network and difficult sample mining method. The method comprises the following steps: step 1, extracting trunk features of a current image byusing a trunk network; 2, constructing a neck network, and generating a feature pyramid; 3, constructing a region generation network model, and generating a region of interest; 4, generating trainingdata based on difficult sample mining; 5, cutting the region features by using a region-of-interest alignment module; and step 6, classifying and positioning a target by using a head network. According to the method, the small target detection precision can be remarkably improved, and only little calculation overhead is increased.

Description

technical field [0001] The present invention relates to the problem of small target detection, and more specifically, relates to a network and difficult sample mining method for small target detection. Background technique [0002] Benefiting from excellent flexibility and portability, UAV aerial photography is widely used in agriculture, film and television, surveying and mapping, surveillance, express delivery, outdoor search and rescue and other fields. The automatic processing and intelligent identification of aerial data has become an urgent need of the industry. As one of the key technologies, UAV target detection has become a current research hotspot. [0003] Although deep learning methods have achieved great success in the field of general object detection, their performance in UAV aerial photography scenes is not satisfactory. Different from ordinary images, UAV aerial images face serious small target challenges: more small targets, lower target average resolutio...

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/25G06V10/44G06V2201/07G06N3/045G06F18/214G06F18/241
Inventor 周靖凯刘琼
Owner SOUTH CHINA UNIV OF TECH
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