An image small target detection method based on combination of two-stage detection

Abstract

The invention discloses a small target detection method based on combination of two-stage detection. The method includes: Sending the original image into a first detector to detect a first-stage target B1; Fusing the output features of the shallow CNN and the output features of the deep CNN to obtain M1 ', and selecting a corresponding feature map M2 from the M1' by using B1; taking the M2 as an input feature map and sending the M2 to an RPN module and a classification and regression module of a second-stage detector for detection and positioning of a second-stage target; And adding d loss obtained from two-stage detection as the total Loss of the whole network to obtain an end-to-end detection network model. According to the invention, a two-stage detection network is constructed; A largetarget is accurately detected firstly, then a small target is detected in a large target area, and a detection frame of the small target is limited in a local area which is most possible and most easily detected, namely the area where the large target is located, so that complex background interference is effectively removed, the false detection probability is reduced, and the detection precisionof the small target and the small target in the image is improved.

Description

technical field [0001] The invention relates to a method in the field of target detection in an image, in particular to a small target detection method in an image based on the combination of two-stage detection. Background technique [0002] Object detection and recognition in images has a wide range of practical needs in applications such as intelligent video surveillance, and it is also a popular research direction in the field of computer vision. Due to the following difficulties and challenges in the existing image target detection algorithm, the detection results still need to be improved: a smaller target is detected in a larger image, due to the shooting distance, the picture is larger but the target size is smaller , after the deep learning convolutional neural network is reduced, the features of the target area are few, and it is difficult to perform effective detection and recognition. [0003] At present, more mature target detection algorithms can be basically ...

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Problems solved by technology

[0007] At present, there is no description or report of the similar technology of the present invention, and no similar data at home and abroad have been collected yet.

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