Ship Detection in Synthetic Aperture Radar Imagery: An Active Contour Model Approach in Computer Vision Deep Learning

Abstract

The utilization of Synthetic Aperture Radar (SAR) images for ship recognition holds significant importance within the realm of maritime surveillance and security. SAR images are useful for ship detection and recognition because they can penetrate through clouds and capture detailed information about ships, such as their size, shape, and orientation. In the context of ship recognition using SAR images, the primary objective is to employ automated methods for the identification and categorization of ships present in SAR imagery. The detection of ships in SAR images is a significant research area, but it remains challenging due to speckle noise, land clutters, and low signal-to-noise ratio. Researchers have developed various approaches to overcome this challenge, such as adaptive filtering, speckle reduction, and segmentation techniques. Hence, a ship detection method is devised that combines the active contour method and the YOLO-v8 model using deep learning techniques. In the first step, the SAR images undergo pre-processing and normalization, and the model is trained with the backbone network. The YOLO-v8 model, renowned for its proficiency in object detection, is applied to delineate precise bounding boxes around ships within the images. The results obtained from experiments conducted on a variety of SAR images convincingly demonstrate that the suggested approach attains proficient ship target detection, striking a balance between accuracy and comprehensiveness. This approach represents a promising solution to enhance ship detection in challenging SAR scenarios.