An efficient SAR image detection based on deep dense-mobile net method

Nowadays, many studies are based on the Synthetic aperture radar (SAR) for detecting a ship which is more important for its safety. This method is used to safeguard the ship from lousy weather and reach it on its time. In recent times, the studies are advanced with deep learning for very sensitive calculation with higher accuracy skills; the SAR image is also worked on it. In this brief, the proposed deep learning of hybrid DenseNet and mobile (DMN) is implemented to detect the ship. This technique is based on the convolutional neural network (CNN), a multi-layer classifier. This method combines DenseNet and mobile techniques that are used to classify the missing ship and detect the false alarm with higher precision. The results are simulated and compared with the previous method by validating parameters like precision, recall, and F1 score. The results showed that the proposed DMN is much more effective than the previous deep learning in ship detection.