Scene and target identification method and device based on multi-task learning

Abstract

The invention relates to a scene and target identification method and device based on multi-task learning. The method comprises the steps that pictures containing different scenes and targets are collected as image sample data; the image sample data is subjected to manual label marking, and target class labels and scene class labels are obtained; a multi-layer convolutional neural network model is built, and network initialization is conducted; the image sample data and the corresponding target class labels are adopted for pre-training the built model till convergence, and a target identification model is obtained; based on a multi-task learning technology, network branches are added into a specific layer of the target identification model, random initialization is conducted, and a multi-task network is obtained; the image sample data and the corresponding scene class labels and target class labels are adopted for e-training the multi-task network till convergence, and a multi-task learning model is obtained; new image data is input to the multi-task learning model, and classification results of scene and target identification of images are obtained. Accordingly, the single task identification precision is improved.

Description

technical field [0001] The invention relates to the combination of the field of vision, image recognition and deep learning, in particular to a method and device for scene and target recognition based on multi-task learning. Background technique [0002] With the rise of deep learning, more and more technologies use deep learning to realize image recognition of pictures or video streams. Compared with traditional methods, deep learning avoids the complexity of manual parameter adjustment and manual feature selection. By building a deep network model, multi-layer analysis and abstract feature extraction are performed on data, which has high accuracy, high reliability, and high Adaptive features. Common image recognition applications cover action recognition, face recognition, object recognition, scene recognition, etc. Among them, object recognition and scene recognition, as the basis of image retrieval, image classification, scene understanding, and environment perception,...

AI Technical Summary

Problems solved by technology

However, since the scene is related to multi-layer information such as the target, the background environment, and the spatial layout, the scene features of the same category have strong variability, and the scene features of different categories may have similar points overlapping each other, which brings great difficulties for accurate recognition. great challenge

At present, the overall scene recognition based on cutting-edge deep learning technology can only achieve a top-1 accuracy rate of about 50%, and it is difficult to play an effective role in practical applications.

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