A Faster RCNN target detection method based on refractory sample mining

Abstract

The invention discloses a Faster RCNN target detection method based on refractory sample mining. The method comprises the steps of 1, image target detection based on deep learning; 2, on the basis ofan online sample mining method, adjusting and setting adopted key parameters as follows: step 3, negative difficult-to-distinguish sample mining: on the basis of a difficult-to-distinguish sample, adjusting a mini-formed by an RPN in training; wherein the ratio of the positive sample to the negative sample of the bch is 1: 3; and step 4, removing redundant frames to avoid loss of multiple calculations. An improved non-maximum suppression algorithm is adopted to reasonably remove redundancy from a suggestion box generated by an RPN layer network. Under the condition that samples are not expanded. The definition of negative samples is broadened, and more samples difficult to train are mined online through the samples. The ratio of positive samples to negative samples is set, and rare sampleswith the maximum loss and difficult training are reasonably and simply calculated; and the loss of classification and frame regression is balanced, so that the continuous reduction of training loss can be met.

Description

technical field [0001] The present invention relates to a method based on the combination of online sample mining technology (OHEM) and negative hard sample mining (HNEM). technical background [0002] In recent years, with the rapid development of computer science and technology, image processing and image target detection based on computer technology have also achieved unprecedented rapid development. Among them, deep learning extracts key target features by learning massive digital image features. It has surpassed human beings, bringing one surprise after another to the industry. The two major tasks of computer vision are image classification and target location detection, while the traditional method of image feature detection relies on experienced algorithm engineers to design matching templates corresponding to targets, such as deformable model (DPM), HOG feature extraction, etc. The former's Target detection is to locate the target through a sliding window, and then ...

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Problems solved by technology

[0003] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a Faster RCNN target detection method based on difficult-to-segment sample mining for the sample problem. The combination of sample mining technology enables the model to learn the characteristics of the existing difficult samples in a targeted manner, and achieve the effect of further improving the generalization and robustness of the model

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