Page 219 - Ai_C10_Flipbook
P. 219

CARGO SHIP
                                                                                               STEAM BOAT
                                                                                               CRUISE     Cargo Ship  17%
                                                                                                          Steam Boat  8%
                                                                                               FISHING BOAT  Cruise  75%
                                          INPUT  CONVOLUTION - ReLU  POOLING  CONVOLUTION - ReLU  POOLING  FLATTEN  FULLY   SOFTMAX
                                                                                       CONNECTED
                                                                                                          Output Probability
                                                           FEATURE LEARNING           CLASSIFICATION
                   Input Image
                                                     Image Processed by CNN
                 In the above diagram, an input image is provided, processed through a CNN, and a prediction is generated based
                 on the labels in the corresponding dataset.

                         Layers of Convolutional Neural Network (CNN)



                 The different layers of a Convolutional Neural Network (CNN) are shown in the following figure:




                                                                                                           CARGO SHIP
                                                                                                           STEAM BOAT
                                                                                                           CRUISE



                                                                                                           FISHING BOAT

                           INPUT  CONVOLUTION - ReLU  POOLING  CONVOLUTION - ReLU  POOLING  FLATTEN  FULLY   SOFTMAX
                                                                                               CONNECTED
                                                      FEATURE LEARNING                        CLASSIFICATION
                 A convolutional neural network consists of the following layers:
                 •  Convolution Layer  • Rectified Linear Unit (ReLU)  • Pooling Layer  • Fully Connected Layer
                 Convolution Layer

                 The Convolutional Layer is the first layer in a Convolutional Neural Network (CNN) and plays a crucial role in
                 processing visual data, such as images. Its main objective is to extract key features from the input image, starting
                 with low-level features like edges, textures, colours, and gradients. These features serve as the building blocks for
                 the network to understand the content of the image.
                 In a CNN, this layer is not limited to just one convolutional operation. As the network deepens, additional
                 convolutional  layers  are  added,  each  progressively  capturing  more  complex  patterns  or  high-level  features
                 such  as  shapes,  objects,  and  contextual  patterns.  This  enables  the  CNN  to  evolve,  recognising  increasingly
                 sophisticated and abstract features, ultimately allowing the network to understand entire images.
                 The convolution operation applies multiple kernels to an image to extract various features from the image. The
                 result of this process is called a feature map (or activation map), which highlights the important features detected
                 by the kernels. The feature map has several key functions:
                    • Image Size Reduction: It reduces the image size, often through pooling layers that follow convolutional layers,
                   making it easier and faster to process while retaining crucial information.
                    • Focus on Relevant Features: It helps focus on the most important features needed for further image processing.
                   For example, in plant disease detection, the model may only need to focus on the patterns on the leaves (such
                   as discolouration or spots) rather than analysing the entire plant. By emphasising the specific features of the
                   leaves, the model can efficiently and accurately detect diseases like fungal infections or bacterial blight, even in
                   a crowded field of plants.

                                                                                             Computer Vision    217
   214   215   216   217   218   219   220   221   222   223   224