A fine-grained image detection method and system based on improved ra-cnn

Abstract

The invention discloses a fine-grained image detection method based on improved RA-CNN: S1, preprocessing the training image to obtain its image vector encoding and category vector encoding; S2, according to the image vector encoding and category vector encoding of the training image, Use the improved RA‑CNN model for weakly supervised training to obtain the predicted bounding box information; S3, use the training picture marked with the bounding box as input, compare the bounding box predicted in step S2 with the marked bounding box, and calculate the loss function to carry out strong supervision training to obtain the trained image detection model; S4, perform grayscale processing and vector normalization processing on the image to be detected to obtain the image vector code of the image to be detected, and input the image vector code of the image to be detected into the above-mentioned experience The trained image detection model obtains the object category and bounding box information in the image to be detected. The invention also provides a corresponding fine-grained image detection system based on the improved RA-CNN.

Description

technical field [0001] The invention belongs to the technical field of target detection, and more specifically relates to a fine-grained image detection method and system based on improved RA-CNN. Background technique [0002] Since the convolutional neural network has emerged in computer vision, the research on deep learning has become more and more popular, and algorithms have emerged in an endless stream. Regarding the classification and positioning of fine-grained image objects, before the emergence of convolutional neural networks, most of them need to rely on a large number of manual annotations to mark the position of objects in the image and accurate local information, and then perform feature construction on highly distinguishable regions. The model is then classified with a classifier. The representative is a feature encoding method based on local regions proposed by Berg et al., which can automatically discover the most discriminative information. After the emer...

AI Technical Summary

Problems solved by technology

After the emergence of convolutional neural networks, research based on strong supervision has risen on a large scale. R-CNN (Region-CNN, regional convolutional neural network) uses selective search to avoid violent enumeration of candidate regions, but since each frame must After one pass of classification, there are many repeated calculations of feature maps

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