Deep Learning-Based Object Detection/Classification

Dear Colleagues,

Object detection and classification are two important tasks in computer vision, and their algorithm, architecture, system and application scope are very wide. Here are some common application scenarios:

1. Algorithm: Object detection and classification algorithms are used to identify and classify objects in images or videos. Common object detection algorithms include YOLO, Faster R-CNN, SSD, etc., and common object classification algorithms include ResNet, VGG, etc. These algorithms can be used in many fields such as human–computer interaction, security monitoring, autonomous driving, smart home and manufacturing.
2. Architecture: Object detection and classification architecture mainly refers to the computer system used to process images or videos, including hardware architecture and software architecture. Hardware architecture usually includes processors, GPUs, FPGAs, etc., and software architecture includes operating systems, programming languages, libraries, etc. The selection of appropriate architecture can improve the efficiency and accuracy of object detection and classification tasks.
3. System: Object detection and classification systems can be applied to many fields. For example, human–computer interaction systems can detect and classify human actions and other behaviors in images or videos to achieve communication between people and machines; security monitoring systems can monitor the behavior of people in the video to identify and classify the behavior to achieve intelligent alarms; autonomous driving systems can detect and classify the objects in the road to help the autonomous vehicle identify traffic signals and obstacles; smart home systems can recognize the location and activity of users to automatically adjust the indoor environment; manufacturing systems can detect whether there is a defect or error in the manufactured product according to the image information of the product, and classify and count the types and quantities of defects to help enterprises improve production quality.
4. Application: Object detection and classification applications are very extensive, including but not limited to human–computer interaction, security monitoring, autonomous driving, smart home, manufacturing and other fields. For example, in human–computer interaction, object detection and classification can be used to identify human actions and other behaviors in images or videos, so as to achieve communication between people and machines; in security monitoring, object detection and classification can be used to monitor the behavior of people in the video to identify and classify the behavior to achieve intelligent alarms; in autonomous driving, object detection and classification can be used to detect and classify the objects in the road to help the autonomous vehicle identify traffic signals and obstacles; in smart homes, object detection and classification can be used to recognize the location and activity of users to automatically adjust the indoor environment; in manufacturing, object detection and classification can be used to detect whether there is a defect or error in the manufactured product according to the image information of the product, and classify and count the types and quantities of defects to help enterprises improve production quality.

In short, object detection and classification have broad application prospects in many fields of computer vision technology, which can help people better understand the visual world, improve work efficiency and quality of life.

Dr. Kuo-Kun Tseng
Guest Editor

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• object detection
• object classification