Fishing and Military Ship Recognition using Parameters of Convolutional Neural Network

Indonesia has a maritime boundary that vulnerable to illegal activities. Those activities lead to the bad loss of Indonesia income. Therefore, monitoring of every object which is passing through the maritime boundary is important. Detection of ship that is passing through the ocean is one of many ways to monitor the maritime boundary. Nowadays, there are many systems developed to detect and to recognize ship automatically especially fishing ship and military ship. The recognition adopts technology which is called CNN. CNN is deep learning algorithm that is based on image. CNN has many parameters that can be optimized the recognition system. This study investigated some parameters such as pooling layer, batch normalization and dropout parameters. For the best accuracy results on the fishing ship and military ship dataset obtained a value of 99.99% for training and 90% for validation accuracy. The best accuracy results are obtained by using the pooling layer with the max pooling type. Max pooling is more efficient used for object recognition than average pooling. The use of dropout functions can increase the level of training accuracy. Batch normalization can increase the validation accuracy value.