Image automatic annotation technology based on deep learning

Author: Zen and the Art of Computer Programming

1 Introduction

Automatic image annotation (Image Annotation) is an important research direction in the field of computer vision. At present, AI technologies such as machine learning and deep learning are gradually being applied to the field of image recognition. Since many tasks have different requirements for labeling data, different tasks can choose different labeling methods. For example, for visual object detection tasks, different kinds of objects need to be labeled, while for image segmentation tasks, different regions need to be labeled differently.

This article will introduce an automatic image annotation technology based on deep learning - Mask RCNN. Mask R-CNN is an end-to-end training method for object detection and image segmentation tasks using deep neural networks. Its characteristics mainly include the following aspects:

1. Feature extraction using FPN (Feature Pyramid Network). The network can simultaneously extract features from images of different sizes and fuse them into a final feature map.
2. Object detection is performed using a standard convolutional neural network as the backbone. The network can extract high-level features and directly regress to bounding box coordinates during object detection.
3. A differentiable RoI pooling module is provided, which can realize feature extraction of ROIs of different sizes.
4. In the prediction stage, the bounding box regression loss function and the mask classification loss function are introduced, and the target detection results are optimized by updating the network parameters.

Since Mask R-CNN can not only perform target detection, but also perform image segmentation tasks, it is widely used in medical image segmentation, remote sensing image extraction and other fields.

2. Related background knowledge

2.1 Mask R-CNN model structure

First, understand the Mask R-CNN model structure.

Mask R-CNN consists of two stages:

1. Detection stage: This stage mainly uses the standard convolutional neural network to perform feature extraction and target detection on the input image.

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