For that purpose, we proposed a method for automatic object detection based on a convolutional neural network. A novel two-stage approach for network training is implemented and verified in the tasks ...

Abstract: The convolutional neural networks (CNNs) have recently demonstrated to be a powerful tool for object detection. However, with the complex scenes in remote sensing images, feature extraction ...

Object detection is one of the fundamental problem in computer vision. Given an image, the goal is to detect ... 2 is employed after each of the first two convolutional layers. We have trained the ...

Considering that traditional image processing approaches rely heavily on feature extraction operators and are not robust enough, we conduct a detailed study of convolutional neural networks. In object ...

With the development of artificial intelligence, the algorithms of convolutional neural network ... Image classification is the popular application of CNN algorithms. Recently, scientists tried to ...

All convolution layers are used to extract the input image feature. The model also has 2 max pooling layers used to reduce the dimensions of the output volume. The last two layers are the flattened ...

Convolutional neural networks (CNNs) are a class of deep neural networks commonly used in computer vision tasks such as image and video recognition, object detection and image segmentation.

Most object-detection ... layer of the neural network encodes specific features from the input image. Finally, the fully connected layers flatten the output of the final convolution layer and ...