Author: Zen and the Art of Computer Programming
overview
Object Detection is an important task in the field of computer vision. It is used to identify, locate and classify specific objects in images or videos. At present, the target detection algorithm based on deep learning has become one of the hottest research directions in the field of computer vision today. This article will combine the author's many years of relevant work experience and undergraduate education, introduce the following target detection algorithms based on deep learning in simple language and graphical representation, and give its typical application scenarios, including face detection, pedestrian detection, vehicle detection, etc. Then, the complete algorithm flow is described by Python language, and the implementation code based on the Tensorflow framework is given, and the training, verification and testing process of the model are introduced in detail. Finally, we will also demonstrate the high-speed operation capability of deep learning-based object detection algorithms on mobile devices through several practical cases. This paper comprehensively introduces the latest progress of the current deep learning-based target detection algorithm, the main research background and technical implementation methods involved, which is of great reference value for understanding and mastering the latest progress of deep learning technology in the field of target detection.
Deep Learning Technology and Its Applications
Deep learning is a type of machine learning technology that uses a large number of neural network layer structures to achieve complex feature extraction and reasoning. Deep learning has the following advantages:
- Fewer model parameters: In many cases, the number of parameters of a deep learning model is much smaller than that of a traditional machine learning model. Therefore, deep learning models can still achieve good results with less training data.
- Data-driven: The deep learning model can automatically learn the characteristics of the data, so that there is no need to manually design complex feature engineering.
- Models are easy to deploy: Since deep learning models are usually relatively simple and have fewer model structural parameters, they can often run on a variety of platforms and achieve better results online.
Deep learning technology and its applications are widely used in image recognition, speech recognition, natural language processing and other fields. For example, image recognition systems usually adopt