SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale
  SAR Object Detection
Yuxuan Li Xiang Li Weijie Li Qibin Hou Li Liu Ming-Ming Cheng Jian Yang
Synthetic Aperture Radar (SAR) object detection has gained significant
attention recently due to its irreplaceable all-weather imaging capabilities.
However, this research field suffers from both limited public datasets (mostly
comprising <2K images with only mono-category objects) and inaccessible source
code. To tackle these challenges, we establish a new benchmark dataset and an
open-source method for large-scale SAR object detection. Our dataset,
SARDet-100K, is a result of intense surveying, collecting, and standardizing 10
existing SAR detection datasets, providing a large-scale and diverse dataset
for research purposes. To the best of our knowledge, SARDet-100K is the first
COCO-level large-scale multi-class SAR object detection dataset ever created.
With this high-quality dataset, we conducted comprehensive experiments and
uncovered a crucial challenge in SAR object detection: the substantial
disparities between the pretraining on RGB datasets and finetuning on SAR
datasets in terms of both data domain and model structure. To bridge these
gaps, we propose a novel Multi-Stage with Filter Augmentation (MSFA)
pretraining framework that tackles the problems from the perspective of data
input, domain transition, and model migration. The proposed MSFA method
significantly enhances the performance of SAR object detection models while
demonstrating exceptional generalizability and flexibility across diverse
models. This work aims to pave the way for further advancements in SAR object
detection. The dataset and code is available at
https://github.com/zcablii/SARDet_100K.