An Integrated Method for Fast Imaging and Detection of Lightweight Intelligent Ship Targets

Ship target detection based on SAR images is an important means of marine observation. Traditional target detection requires the most processing time to image the SAR echo. Considering the sparse distribution of ship targets in wide-swath marine SAR images, imaging and detecting processes on non-target regions seriously reduce efficiency. This paper proposes an integrated framework to improve marine SAR imaging detection efficiency by adding two steps of selection for target areas. Firstly, an RC-TextCNN network is designed to select target areas on azimuth direction from SAR echo one-dimensional compression data. After imaging selected areas, a dynamic quantization and threshold segmentation method is used to further remove non-target areas. Finally, suspected target areas are introduced into the pruned yolov7 model for final target detection. This workflow significantly minimizes computational and time costs. The experiment on Gaofen3 data shows that the speed of the process is increased by three times while detection accuracy is at 90%.