ADERLNet: Adaptive Denoising Enhancement Representation Learning for Low-Latency and High-Accurate Target Detection on SAR Sensors

SAR image detection results

Speed-accuracy trade-offs

Performance

Note: All the pre-trained ADERLNet weight links lead to a Baidu Netdisk address with the extraction code: bahy.

On SSDD

Model	size	AP (%)	AP50 (%)	AP75 (%)	FPS	Params (M)	FLOPs (G)
ADERLNet-CW	640	0.698	0.985	0.855	95.24	38.96	105.20
aggregated ADERLNet-CW	640	0.684	0.983	0.795	129.87	37.31	103.80
ADERLNet-CS	640	0.691	0.977	0.831	94.34	37.73	104.20
aggregated ADERLNet-CS	640	0.679	0.976	0.811	123.46	36.89	103.50

On MSAR-1.0

Model	size	AP (%)	AP50 (%)	AP75 (%)	FPS	Params (M)	FLOPs (G)
ADERLNet-CW	640	0.644	0.936	0.685	105.3	38.97	105.20

On SAR-Ship-Dataset

Model	size	AP (%)	AP50 (%)	AP75 (%)	FPS	Params (M)	FLOPs (G)
ADERLNet-CW	640	0.656	0.958	0.778	106.4	38.96	105.20

Quick Start

Installation

Install ADERLNet from source

git clone https://github.com/yangyahu-1994/ADERLNet.git
cd ADERLNet
pip3 install -U pip && pip3 install -r requirements.txt

Details

Testing

python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val

You will get the results:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868

To measure accuracy, download COCO-annotations for Pycocotools to the ./coco/annotations/instances_val2017.json

Transfer learning

Single GPU finetuning for custom dataset

# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml

# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

References

Thanks to their great works:

• WongKinYiu/yolov7