International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 06 | June 2022
e-ISSN: 2395-0056
www.irjet.net
p-ISSN: 2395-0072
YOLO BASED SHIP IMAGE DETECTION AND CLASSIFICATION Mrs.S.Ponnarasi 1, S.Kiruba Shankar2, N.Mohamed Siddiq3, K.Prasanth4 1ASSISTANT
PROFESSOR,B.TECH-IT,AAMEC,THIRUVARUR,INDIA B.TECH-IT,FINAL YEAR,AAMEC,THIRUVARUR,INDIA ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 METHODLOGY Abstract - Considering the important apps in the 2,3,4STUDENT,
military and the civilian domain, ship identification and based on optical remote sensing images considerable observation in the sea surface remote sensing filed. An improvement algorithm of SVM (support vector machine) with CNN algorithm is proposed here for fine tuning system. Multi class Image classification using CNN and SVM on a Kaggle data set. Please clone the data set from Kaggle using the following command. The deep convolutional neural network (DCNN) technique takes the feature extraction of input images for input. The features are acquired from the convolutional neural network and are moved to the support vector machine (SVM) classifier. In this research claimed that replacing the trainable classifier (the conventional SoftMaxfunction) of a CNN model with an SVM classifier can raise the classification performance. Its improve the accuracy of system (98percent)
DEEP LEARNING Traditional machine learning methods struggle to perform as the dimensionality of the input data increases. For example, in image classification input images may contain thousands of pixel values. This problem is called the curse of dimensionality to overcome this problem, traditional methods require manual feature engineering of raw data in order to decrease the input dimensions. However, in practice, engineering these features from a mass of data is hard without specific domain expertise. This is where deep learning steps in. Deep learning solves the curse of dimensionality by learning hierarchical representations of the data with complex models of multiple layers. This is achieved with representation learning. In representation learning, no feature engineering is used. Instead, a model learns by itself which features of the mass of data are relevant for the task. When a deep learning model is trained, the model learns abstract representations of the data by observing the data patterns. The first layers of a deep learning model learns more simple features of the data, whereas the last layers learn more specific features of the data.
Key Words: Deep Learning, CNN
1. INTRODUCTION The maritime vessel classification, verification and identification are critical and challenging problems regarding national defense and marine security for coastal countries. These countries have to control the traffic and they are constantly trying to improve efficiency on ports for economic growth. There are also other threats such as stealing, sea pollution and illegal fishery. Most of these issues are not only related with countries individually, but also they need to be observed in a global perspective. That’s why the International Maritime Organization (IMO) was established. IMO is a special agency of the United Nations, whose purpose is to create regulatory framework for the safety and maintenance of shipping and also protect marine pollution caused by ships. These systems can be used in control centers for assist.
1.2 Convolutional Neural Network(CNN) In deep learning, a convolutional neural network (ConvNet) is a class of deep neural networks, most commonly applied to analyze visual image. Now we think of a neural network we think about matrix multiplications but that is not that case with ConvNet. It uses a special technique called Convolutional. Now in mathematics convolution is a mathematical operation on two functions that produces a third function that show how the shape of one is modified by other.
2. Neural Network
Vessel verification round is deciding whether two vessel images belong to same ship or not. The main application area of this task is marine time surveillance, where vessel passing then it is strictly tracked in straits. In this kind of applications, two separate camera systems are placed to both entry and exit point. The images gathered from those systems are compared to infer whether the ship is still passing through or it has completed its passing.
© 2022, IRJET
|
Impact Factor value: 7.529
The history of neural networks is quite old. Rosenblatt (1958) was a psychologist and he proposed “the perceptron” which is a mathematical model heavily inspired by biological neurons in human brain. As we can see in Figure 2.1, the model has n binary inputs and exactly same number of weights. Each input value is multiplied by the corresponding weight. If the sum of
|
ISO 9001:2008 Certified Journal
|
Page 978