With the continuous advancement of deep learning technology, revolutionary changes have taken place in the field of machine vision. Deep learning algorithms have shown unprecedented results in understanding images and videos, especially in the three core tasks of image classification, target detection and semantic segmentation. This article will explore the technical points, usage scenarios, and connections between these three tasks from the perspective of a deep learning algorithm engineer.
Image Classification
Image classification is a fundamental task in deep learning, which aims to assign images into predefined categories. The task of image classification is relatively simple. It only needs to identify the main content in the image. It does not need to locate or segment the specific location of the object.
Technical points:
1. Convolutional Neural Network (CNN): CNN is the most commonly used deep learning model in image classification. It extracts image features through multiple convolutional layers and pooling layers, and performs classification through fully connected layers.
2. Data enhancement: In order to make the model have better generalization ability, various transformations are usually performed on the training data, such as rotation, scaling, cropping, etc.
3. Model structure: From LeNet, AlexNet to VGG, Inception, ResNet, etc., innovation in model structure is also the key to improving classification performance.
4. Transfer learning: When the amount of data is insufficient, pre-trained models can be used through transfer learning to transfer existing knowledge to improve performance.
scenes to be used:
Image classification is widely used in content retrieval, security monitoring, medical diagnosis, autonomous driving and other fields. For example, in medical diagnosis, image classification can help identify abnormal areas in X-ray or MRI images; in autonomous driving, it can classify obstacles on the road.
Object Detection
Object detection involves not only identifying objects in an image, but also determining their location and size, usually expressed in the form of bounding boxes.
Technical points:
1. Two-stage detectors: such as R-CNN, Fast R-CNN and Faster R-CNN, first generate candidate regions (region proposals), and then perform classification and bounding box regression on these regions.
2. Single-stage detectors: such as YOLO and SSD, predict categories 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-maximum suppression (NMS): used to remove redundant bounding boxes and retain the optimal detection results.
scenes to be used:
Object detection is widely used in video surveillance, unmanned retail, intelligent transportation and other fields. For example, in intelligent transportation systems, target detection can be used to identify and track pedestrians and vehicles to achieve traffic flow control and accident prevention.
Semantic Segmentation
Semantic segmentation aims to classify each pixel in the image and achieve the precise boundary delineation of each object in the image.
Technical points:
1. Fully Convolutional Network (FCN): Replaces the fully connected layer in the traditional CNN with a convolutional layer, so that the network can accept input images of any size and output segmentation maps of corresponding sizes.
2. Upsampling and skip connection: Through the upsampling and skip connection structure, FCN can combine low-level detailed information and high-level semantic information to improve the accuracy of segmentation.
3. Segmentation network architecture: such as U-Net, SegNet, DeepLab, etc. These architectures improve segmentation performance through special designs.
4. Conditional Random Field (CRF): a post-processing step used to optimize the details of segmentation and make the boundaries clearer.
scenes to be used:
Semantic segmentation has important applications in medical image analysis, autonomous driving, robot perception, etc. For example, in the field of autonomous driving, semantic segmentation can help vehicles accurately identify road surfaces, pedestrians, vehicles, etc. at the pixel level, thereby achieving safe navigation.
Image classification, target detection and semantic segmentation are the three core tasks of deep learning in the field of machine vision. They respectively solve the problems of "what", "where" and "where is the specific boundary". Although these tasks differ in technology and application, they all rely on the powerful feature extraction capabilities of deep learning models. As technology continues to develop, the boundaries of these tasks are gradually blurring. For example, the combination of object detection and semantic segmentation creates the instance segmentation task. In the future, with the further innovation of algorithms and the improvement of computing resources, the application of deep learning in the field of machine vision will become more extensive and in-depth.