Morphological Grayscale Pre-processing to SAR Images for Reducing Noise in Ship Detection Based on YOLOv8

The development of a ship detection system using SAR pictures loaded with noise poses issues for pictures Intelligence (IMINT). The YOLOv8 model is utilized for ship identification. The preprocessing approaches entail employing a fusion of grayscale morphology techniques and image restoration using a harmonic mean filter and a bandpass. This technique is designed to assess the effect of noise reduction to enhance the accuracy of detecting objects in SAR images. The preprocessing technique is categorized into two methods: basic grayscale morphology (GM1-GM6) and a fusion of image restoration with grayscale morphology (GHB1-GHB6). The model's performance is assessed using mAP and IoU criteria. This research discovered that ship objects were not detected successfully in the presence of several types of noise. These failures were attributed to factors such as tiny ship size, low picture quality, and inadequate preprocessing techniques for noise handling. The findings indicate a substantial enhancement in ship detection, specifically in synthetic aperture radar (SAR) images affected by sidelobe noise. There were noticeable enhancements in the accuracy of images that underwent preprocessing using GHB5. GHB5 employs a combination of image restoration, closure, and erosion techniques.