Benchmarking SAR Target Detection Networks and Analysis of Degradation Depending on the Phase Error

SAR images are more difficult to analyze and acquire than optical images; therefore, high-quality datasets for deep learning models are insufficient. In this study, a new detection dataset was generated based on the MSTAR dataset to train detection networks for ground military targets. To verify the dataset and analyze the performance of the ground target detection network in SAR images, various models were used to compare the detection ability, resulting in an overall good performance of mAP 0.8 or higher. In addition, we analyzed the performance change trends if phase errors were included in the test images. In terms of image and detection performance degradation, we determined an acceptable range for the phase error that did not significantly reduce the detection performance. This is expected to contribute to the creation of more robust networks when building SAR image datasets and models in the future.