With the continuous advancement of deep Xi technology, the field of machine vision has undergone revolutionary changes. The algorithm of the deep learning Xi has shown unprecedented results in the understanding of images and videos, especially in the three core tasks of image classification, object detection and semantic segmentation. This article will explore the technical points, usage scenarios, and relationships between these three tasks from the perspective of a deep Xi algorithm engineer.
图像分类(Image Classification)
Image classification is a fundamental task in deep Xi that aims to assign images to predefined categories. The task of image classification is relatively simple, only need to identify what the main content of the image is, and does not need to locate or segment the specific position of the object.
Technical Highlights:
1. Convolutional Neural Network (CNN): CNN is the most commonly used deep Xi model in image classification, which extracts the features of an image through multi-layer convolutional layers and pooling layers, and classifies them through fully connected layers.
2. Data augmentation: In order to make the model have better generalization capabilities, various transformations are usually performed on the training data, such as rotation, scaling, and cropping.
3. Model structure: From LeNet and AlexNet to VGG, Inception, ResNet, etc., the innovation of model structure is also the key to improving classification performance.
4. Transfer Xi: When the amount of data is insufficient, you can use the pre-trained model in the form of transfer Xi to transfer the existing knowledge to improve performance.
Usage Scenarios:
Image classification is widely used in many fields such as content retrieval, security monitoring, medical diagnosis, and autonomous driving. For example, in medical diagnostics, image classification can help identify abnormal areas in X-ray or MRI images, and in autonomous driving, obstacles on the road can be classified.
目标检测（Object Detection）
Object detection not only recognizes objects in an image, but also determines their location and size, usually in the form of a bounding box.
Technical Highlights:
1. Two-stage detectors, such as R-CNN, Fast R-CNN, and Faster R-CNN, first generate region proposals, and then classify and regress these regions.
2. Single-stage detectors, such as YOLO and SSD, predict classes and bounding boxes directly in a single network, which is faster, but may sacrifice some accuracy.
3. Anchor boxes: Used to predefine bounding boxes of different sizes and proportions to improve the performance of the detector.
4. Non-Maximal Suppression (NMS): Used to remove redundant bounding boxes and retain the optimal detection results.
Usage Scenarios:
Object detection has a wide range of applications in video surveillance, unmanned retail, intelligent transportation and other fields. For example, in intelligent transportation systems, object detection can be used to identify and track pedestrians and vehicles for traffic flow control and accident prevention.
语义分割（Semantic Segmentation）
Semantic segmentation aims to classify every pixel in an image, enabling the division of precise boundaries of each object in the image.
Technical Highlights:
1. Fully Convolutional Network (FCN): Replace the fully connected layer in the traditional CNN with the convolutional layer, so that the network can accept the input image of any size and output the segmentation map of the corresponding size.
2. Upsampling and hopping joining: Through the upsampling and hopping connection structure, FCN can combine low-level detail information with high-level semantic information to improve the accuracy of segmentation.
3. Segmentation network architectures: such as U-Net, SegNet, DeepLab, etc., which are specially designed to improve the performance of segmentation.
4. Conditional Random Field (CRF): A post-processing step that optimizes the details of the segmentation to make the boundaries clearer.
Usage Scenarios:
Semantic segmentation has important applications in medical image analysis, autonomous driving, and robot perception. For example, in the field of autonomous driving, semantic segmentation can help vehicles accurately identify the road surface, pedestrians, vehicles, etc., at the pixel level, so as to achieve safe navigation.
Image classification, object detection, and semantic segmentation are the three core tasks of deep learning Xi in the field of machine vision, which solve the problems of "what", "where", and "where are the specific boundaries", respectively. Although these tasks differ in technology and application, they all rely on the powerful feature extraction capabilities of deep learning Xi models. As technology continues to evolve, the boundaries of these tasks are blurring, for example, the combination of object detection and semantic segmentation gives rise to instance segmentation tasks. In the future, with the further innovation of algorithms and the improvement of computing resources, the application of deep Xi in the field of machine vision will be more extensive and deeper.