Small target detection method based on multi-scale images and weighted fusion loss

Abstract

The invention belongs to the field of image and video processing, and relates to a small target detection method based on multi-scale images and weighted fusion loss, and the method comprises the steps: extracting a plurality of groups of feature vectors from a plurality of different-scale images based on an improved Mask RCNN model, carrying out the fusion of the plurality of groups of feature vectors, and constructing a feature pyramid; generating a candidate detection box based on the feature pyramid and screening to obtain a suggested detection box; correspondingly returning the suggesteddetection boxes to the feature pyramid to generate feature maps of the suggested detection boxes, and performing aligned interception on the feature maps; inputting the aligned suggested detection boxes into a classifier layer to obtain category confidence coefficients and position offsets of the suggested detection boxes; in the test stage, screening a certain suggested detection box according tothe category confidence score of the suggested detection box, and performing non-maximum suppression; in the training stage, weighting the loss function calculated by detecting the small target feature layer and fusing with the loss function of detecting the large target layer and the middle target layer, thus the sensitivity of the model to the small target object is enhanced.

Description

technical field [0001] The invention belongs to the field of image and video processing, and relates to a small target detection method based on multi-scale images and weighted fusion loss. Background technique [0002] With the development of machine learning and deep learning, relying on the powerful learning ability of convolutional neural network, the fields of pattern recognition and computer vision have received unprecedented attention and enthusiasm. In the era of machine automation and artificial intelligence, the role of cameras is increasingly equal to that of human eyes. The development of computer vision is particularly important, and has attracted extensive attention from industry and academia. Among them, target detection has achieved remarkable results in the field of computer vision and is constantly improving. However, most of the target objects in pictures and videos appear in extremely tiny forms. Usually there are a lot of objects in a frame of pictures...

AI Technical Summary

Problems solved by technology

[0003] The difficulty of small target detection lies in the scale. Inputting the network model with a single size to extract feature information often cannot take care of targets of various scales.

Although the existing Mask RCNN model has achieved good results in target detection, there are still problems such as single input image scale, uncertain resolution, insufficient use of context information, and insensitivity to small target object detection.

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