What is the purpose of using more than one convolutional layer in a convolutional neural network?

 Using many convolutional layers in a convolutional neural network (CNN) allows it to learn increasingly sophisticated and abstract properties from incoming data. Each convolutional layer learns a set of filters to apply to incoming data in order to extract a collection of features. These characteristics are then transmitted to the next layer, which uses a different set of filters to extract higher-level features depending on the preceding layer's features.

The network may learn more and more complicated information by stacking many convolutional layers, which can lead to improved performance on tasks like image classification, object identification, and segmentation. The first layer of a CNN, for example, may learn simple characteristics like edges and corners, while the second layer might learn more complicated features like curves and textures. The third layer may learn even more complicated task-specific information, such as the shapes of objects in a picture.

Furthermore, employing many convolutional layers can aid in reducing the amount of parameters in the network and improving its computing efficiency. This is due to the fact that each layer learns a smaller set of filters than a single huge convolutional layer that tries to learn all characteristics at once.

Overall, utilising many convolutional layers in a CNN helps the network to learn increasingly complicated and abstract characteristics, leading to enhanced performance on a number of computer vision applications.