Fireworks Image Classification with Deep Learning
Chih-Lung Chang, Hsin-Ming Tseng, Hsueh‐Ting Chu · 2021
Before the advent of deep learning, traditional image recognition used algorithms to find features and then classify images using classical machine learning algorithms. But it is difficult to define features for variable types of images. Instead, Representation learning is a way to allow a system to discover the representations of feature for image processing. Deep-learning algorithms attempt to learn multiple levels of representation and play the key role of modern Representation learning. When deep learning neural networks recognize images, the shallow layers usually extract lower-level features, then start with intermediate-level features, and finally the full images. This study uses CNN (Convolutional Neural Network) to classify fireworks images, and the trained modules are used to evaluate the accuracy and suitability.