Journal of Applied Optics, Volume. 44, Issue 5, 1010(2023)
Multi-scale oriented object detection based on improved RoI Transformer in remote sensing images
Figures & Tables(17)
Fig. 1. Comparison between remote sensing images (the first row) and natural images (the second row)
Fig. 8. Comparison of detection results (objects of large aspect ratios)
Table 1. Performance comparison of different methods on DOTAv1.0 dataset
View tableView in ArticleTable 1. Performance comparison of different methods on DOTAv1.0 dataset
Method Backbone Loss AP/% mAP/% PL BD BR GTF SV LV SH TC BC ST SBF RA HA SP HC One-stage Rotated RetinaNet ResNet50 Smooth L1 89.7 75.0 40.8 64.1 66.5 67.7 85.8 90.7 62.6 65.7 54.4 62.0 62.6 52.2 54.5 66.3 R3Det ResNet50 Smooth L1 89.5 73.2 44.4 65.3 66.9 77.2 87.2 90.8 57.9 66.2 51.3 63.2 72.1 53.0 54.6 67.5 S2ANet ResNet50 Smooth L1 89.0 73.8 43.6 67.1 64.9 74.2 79.1 90.5 62.7 66.3 56.8 64.8 61.2 54.2 42.0 66.0 SASM reppoints ResNet50 GIoU 89.5 76.0 45.3 70.7 59.9 74.6 78.0 90.3 64.1 67.3 46.2 67.1 70.3 56.3 44.3 66.7 Oriented reppoints ResNet50 GIoU 89.7 75.7 49.8 70.7 74.1 80.5 88.4 90.5 65.1 68.6 47.1 64.6 70.4 57.8 54.6 69.8 Two-stage Rotated Faster RCNN ResNet50 Smooth L1 88.5 74.7 44.1 70.0 63.7 71.4 79.4 90.5 58.7 62.0 54.7 64.5 63.2 58.2 50.1 66.3 Oriented RCNN ResNet50 Smooth L1 89.1 75.8 50.0 68.3 62.3 84.0 88.8 90.6 68.7 62.3 57.0 63.6 66.4 57.3 39.1 68.2 RoI Transformer ResNet50 Smooth L1 89.4 77.7 46.8 71.9 68.4 77.9 80.0 90.7 71.3 62.5 59.1 63.6 67.3 60.2 45.4 68.8 ReDet ReResNet50 Smooth L1 89.6 78.0 47.4 68.8 65.8 82.4 87.4 90.6 67.5 69.7 63.4 65.9 67.3 53.0 48.7 69.7 Ours HRNet KLD 89.8 75.4 54.7 78.9 68.8 78.6 89.3 90.7 75.7 62.8 67.0 67.2 75.3 60.7 52.1 72.5
Table 2. Performance comparison of different methods on DIOR-R dataset
View tableView in ArticleTable 2. Performance comparison of different methods on DIOR-R dataset
Method Backbone Loss AP/% mAP/% APL APO BF BC BR CH ESA ETS DAM GF GTF HA OP SH STA STO TC TS VE WM One-stage Rotated Retinanet ResNet50 Smooth L1 59.1 15.0 70.4 81.1 14.5 72.6 64.9 46.6 14.6 70.9 74.7 24.8 30.2 67.0 69.1 50.1 81.2 41.6 32.5 61.9 52.1 Rotated Retinanet-G ResNet50 GWD 64.6 21.1 72.9 81.1 13.1 72.7 68.5 45.8 14.7 70.1 75.1 27.2 30.6 68.9 66.1 57.9 81.2 47.4 34.8 61.5 53.8 R3Det ResNet50 Smooth L1 53.3 27.9 68.9 81.0 22.9 72.6 66.4 49.6 19.2 68.4 76.0 22.1 41.5 68.3 57.9 55.4 81.1 45.5 35.7 54.0 53.4 R3Det-K ResNet50 KLD 57.8 34.9 69.4 81.2 28.5 72.7 71.8 53.2 16.1 71.8 77.1 36.4 47.6 74.5 62.5 60.8 81.3 50.0 39.8 56.2 57.2 S2ANet ResNet50 KFIoU 67.2 28.0 76.0 80.8 27.3 72.6 61.2 60.3 17.9 68.6 78.2 26.2 44.6 77.7 65.8 67.4 81.3 48.9 42.2 63.1 57.8 SASM reppoints ResNet50 GIoU 61.2 52.1 74.5 82.7 32.4 72.5 76.0 58.1 34.9 71.3 77.1 38.6 51.5 79.1 64.8 66.3 80.7 60.5 41.7 64.2 62.0 Oriented reppoints ResNet50 GIoU 68.7 41.9 75.1 84.0 35.4 75.4 79.5 65.8 32.1 75.0 78.6 43.4 51.8 80.3 66.5 66.4 85.4 54.0 46.2 65.0 63.5 Two-stage Rotated Faster RCNN ResNet50 Smooth L1 62.0 18.1 71.3 81.0 22.9 72.5 61.0 58.5 10.0 67.6 78.8 34.3 38.9 80.4 58.8 62.4 81.3 44.7 41.3 64.3 55.5 Oriented RCNN ResNet50 Smooth L1 61.8 26.7 71.6 81.3 33.8 72.6 74.0 58.4 23.7 66.8 80.0 29.9 52.0 81.0 62.5 62.4 81.4 50.6 42.3 65.0 58.9 RoI Transformer ResNet50 Smooth L1 63.1 30.7 71.8 81.5 33.9 72.7 75.8 64.6 24.3 67.4 82.5 35.7 51.1 81.2 70.5 70.8 81.5 44.4 43.4 66.0 60.7 ReDet ReResNet50 Smooth L1 71.0 28.3 71.5 88.7 31.3 72.7 71.6 61.1 20.8 61.8 81.9 36.7 48.8 81.1 63.1 62.5 81.6 49.2 42.8 64.6 59.6 Ours HRNet KLD 63.1 41.6 79.0 88.0 42.1 72.6 76.6 65.8 28.2 71.0 82.9 42.2 57.1 81.3 72.5 70.4 89.7 53.3 49.1 66.3 64.7
Table 3. Detection effects of small object on DOTAv1.0 and DIOR-R datasets
View tableView in ArticleTable 3. Detection effects of small object on DOTAv1.0 and DIOR-R datasets
Method Backbone Loss DIOR-R/% DOTAv1.0/% SH VE WM SV SH One-stage Rotated RetinaNet ResNet50 Smooth L1 67.0 32.5 61.9 66.5 85.8 R3Det ResNet50 Smooth L1 68.3 35.7 54.0 66.9 87.2 S2ANet ResNet50 Smooth L1 77.7 42.2 63.1 64.9 79.1 SASM reppoints ResNet50 GIoU 79.1 41.7 64.2 59.9 78.0 Oriented reppoints ResNet50 GIoU 80.3 46.2 65.0 74.1 88.4 Two-stage Rotated Faster RCNN ResNet50 Smooth L1 80.4 41.3 64.3 63.7 79.4 Oriented RCNN ResNet50 Smooth L1 81.0 42.3 65.0 62.3 88.8 RoI Transformer ResNet50 Smooth L1 81.2 43.4 66.0 68.4 80.0 ReDet ReResNet50 Smooth L1 81.1 42.8 64.6 65.8 87.4 Ours HRNet KLD 81.3 49.1 66.3 68.8 89.3
Table 4. Comparison of effectiveness of KLD and HRNet on DOTAv1.0 dataset
View tableView in ArticleTable 4. Comparison of effectiveness of KLD and HRNet on DOTAv1.0 dataset
Method KLD HRNet mAP/% Rotated Faster RCNN 66.3 RoI Transformer 68.8 Ours(a) √ 70.3 Ours(b) √ 71.7 Ours(c) √ √ 72.5
Table 5. Comparison of effectiveness of KLD and HRNet on DIOR-R dataset
View tableView in ArticleTable 5. Comparison of effectiveness of KLD and HRNet on DIOR-R dataset
Method KLD HRNet mAP/% Rotated Faster RCNN 55.5 RoI Transformer 60.7 Ours(a) √ 61.5 Ours(b) √ 63.9 Ours(c) √ √ 64.7
Table 6. Comparison of mAP for three loss function models
View tableView in ArticleTable 6. Comparison of mAP for three loss function models
Loss Function DOTAv1.0/% DIOR-R/% GWD 69.2 61.4 KFIOU 68.9 60.3 KLD 70.3 61.5
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Minhao LIU, Kun WANG, Ruijiao JIN, Tian LU, Zhang LI. Multi-scale oriented object detection based on improved RoI Transformer in remote sensing images[J]. Journal of Applied Optics, 2023, 44(5): 1010
Paper Information
Category: Research Articles
Received: Jul. 7, 2023
Accepted: --
Published Online: Mar. 12, 2024
The Author Email: LI Zhang (李璋)
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