With the rapid development of deep learning and computer vision, related technologies such as image classification, image target detection, and image segmentation have been widely used in many fields. Object detection is an important part of image understanding. Its task is to find all the objects of interest in the image and determine the position, size and category of the target. It is one of the core basic topics in the field of machine vision.
As a traditional topic of image recognition, image target detection methods have experienced stages based on feature extraction methods and statistical learning methods. With the development of deep learning, the target detection method has entered a new stage and a new height. This time, we will sort out the development process of image target detection methods from the perspective of veterans in the field of image recognition.
Lecturer: Datatang-Data Product Department-Zheng Jilong
Graduated from Beijing Institute of Technology, mainly researching in image target detection, image entity segmentation, digital video editing, etc. He has worked in the National Engineering Laboratory of Digital Video Codec Technology at Peking University and the Institute of Information Engineering of the Chinese Academy of Sciences. Published the paper "CNN Based Vehicle Counting with Virtual Coil in Traffic Surveillance". Currently mainly engaged in research and development of image data products.
1. Introduction to image target detection;
2. Traditional methods for image target detection;
3. Image target detection model based on depth learning;
4. Report on recent relevant practical work.
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