Ships Detection on Aerial Imagery using Transfer Learning and Selective Search - Dewi, Desak Ayu Sista; Sri Arsa, Dewa Made; Susila, Anak Agung Ngurah Hary; Widyantara, I Made Oka (Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi); Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023DO - 10.24843/JIM.2023.v11.i03.p04, 2023)
The traffic in the water area such as harbor and sea strait is highly important to be monitored because it helps to minimize unwanted ships accident. As a result, we proposed an automatic detection method to localize ships contained in sattelite image. We examine several deep learning method as the classification backbone, namely MobileNetV2, DenseNet121, VGG16, and ResNet50. Afterwards, we employed the trained model for detecting the ships. To make the detection faster, inspite of using a sliding window, we use selective search to sample the object candidates from the given scene. The experiments was done using Shipsnet dataset and tested on aerial images. We also conducted a cross domain evaluation where the images were taken using Google Earth. The results indicate that MobileNetV2 has the best performance on classification and detection tasks. The MobileNetV2 is also able to detect the ships on cross-domain scenarios.