Point RCNN: An Angle-Free Framework for Rotated Object Detection
Abstract
:1. Introduction
- We propose Point RCNN, a purely angle-free framework for rotated object detection in aerial images. Without introducing angle prediction, Point RCNN is able to address the boundary discontinuity problem.
- We propose PointRPN as an RPN network, which aims to learn a set of representative points for each object of interest, and can provide better detection recall for rotated objects in aerial images.
- We propose PointReg as an RCNN head, which can responsively regress and refine the four corners of the rotated proposals generated by PointRPN.
- Aerial images are usually long-tail distributed. We further propose to resample images of rare categories to stabilize training and improve the overall performance.
- Compared with state-of-the-art methods, extensive experiments demonstrate that our Point RCNN framework attains higher detection performance on multiple large-scale datasets and achieves new state-of-the-art performance.
2. Materials and Methods
2.1. Related Work
2.1.1. Horizontal Object Detection
2.1.2. Rotated Object Detection
2.2. Methods
2.2.1. Boundary Discontinuity Problem
2.2.2. Overview
2.2.3. PointRPN
2.2.4. PointReg
2.2.5. Balanced Dataset Strategy
3. Results
3.1. Datasets
3.2. Implementation Details
3.3. Main Results
Method | Backbone | PL | BD | BR | GTF | SV | LV | SH | TC |
---|---|---|---|---|---|---|---|---|---|
RoI Trans. * [13] | R101-FPN | 88.64 | 78.52 | 43.44 | 75.92 | 68.81 | 73.68 | 83.59 | 90.74 |
O-DNet * [63] | H104 | 89.30 | 83.30 | 50.10 | 72.10 | 71.10 | 75.60 | 78.70 | 90.90 |
DRN * [64] | H104 | 89.71 | 82.34 | 47.22 | 64.10 | 76.22 | 74.43 | 85.84 | 90.57 |
Gliding Vertex * [26] | R101-FPN | 89.64 | 85.00 | 52.26 | 77.34 | 73.01 | 73.14 | 86.82 | 90.74 |
BBAVectors * [27] | R101 | 88.63 | 84.06 | 52.13 | 69.56 | 78.26 | 80.40 | 88.06 | 90.87 |
CenterMap * [65] | R101-FPN | 89.83 | 84.41 | 54.60 | 70.25 | 77.66 | 78.32 | 87.19 | 90.66 |
CSL * [19] | R152-FPN | 90.25 | 85.53 | 54.64 | 75.31 | 70.44 | 73.51 | 77.62 | 90.84 |
SCRDet++ * [23] | R152-FPN | 88.68 | 85.22 | 54.70 | 73.71 | 71.92 | 84.14 | 79.39 | 90.82 |
CFA [55] | R-152 | 89.08 | 83.20 | 54.37 | 66.87 | 81.23 | 80.96 | 87.17 | 90.21 |
SA-Net * [21] | R50-FPN | 88.89 | 83.60 | 57.74 | 81.95 | 79.94 | 83.19 | 89.11 | 90.78 |
ReDet * [15] | ReR50-ReFPN | 88.81 | 82.48 | 60.83 | 80.82 | 78.34 | 86.06 | 88.31 | 90.87 |
Oriented RCNN * [16] | R101-FPN | 90.26 | 84.74 | 62.01 | 80.42 | 79.04 | 85.07 | 88.52 | 90.85 |
Point RCNN * (Ours) | ReR50-ReFPN | 82.99 | 85.73 | 61.16 | 79.98 | 77.82 | 85.90 | 88.94 | 90.89 |
Point RCNN (Ours) | ReR50-ReFPN | 86.21 | 86.44 | 60.30 | 80.12 | 76.45 | 86.17 | 88.58 | 90.84 |
Point RCNN (Ours) | Swin-T-FPN | 86.59 | 85.72 | 61.64 | 81.08 | 81.01 | 86.49 | 88.84 | 90.83 |
BC | ST | SBF | RA | HA | SP | HC | mAP | ||
RoI Trans. * [13] | R101-FPN | 77.27 | 81.46 | 58.39 | 53.54 | 62.83 | 58.93 | 47.67 | 69.56 |
O-DNet * [63] | H104 | 79.90 | 82.90 | 60.20 | 60.00 | 64.60 | 68.90 | 65.70 | 72.80 |
DRN * [64] | H104 | 86.18 | 84.89 | 57.65 | 61.93 | 69.30 | 69.63 | 58.48 | 73.23 |
Gliding Vertex * [26] | R101-FPN | 79.02 | 86.81 | 59.55 | 70.91 | 72.94 | 70.86 | 57.32 | 75.02 |
BBAVectors * [27] | R101 | 87.23 | 86.39 | 56.11 | 65.62 | 67.10 | 72.08 | 63.96 | 75.36 |
CenterMap * [65] | R101-FPN | 84.89 | 85.27 | 56.46 | 69.23 | 74.13 | 71.56 | 66.06 | 76.03 |
CSL * [19] | R152-FPN | 86.15 | 86.69 | 69.60 | 68.04 | 73.83 | 71.10 | 68.93 | 76.17 |
SCRDet++ * [23] | R152-FPN | 87.04 | 86.02 | 67.90 | 60.86 | 74.52 | 70.76 | 72.66 | 76.56 |
CFA * [55] | R-152 | 84.32 | 86.09 | 52.34 | 69.94 | 75.52 | 80.76 | 67.96 | 76.67 |
SA-Net * [21] | R50-FPN | 84.87 | 87.81 | 70.30 | 68.25 | 78.30 | 77.01 | 69.58 | 79.42 |
ReDet * [15] | ReR50-ReFPN | 88.77 | 87.03 | 68.65 | 66.90 | 79.26 | 79.71 | 74.67 | 80.10 |
Oriented RCNN * [16] | R101-FPN | 87.24 | 87.96 | 72.26 | 70.03 | 82.93 | 78.46 | 68.05 | 80.52 |
Point RCNN * (Ours) | ReR50-ReFPN | 88.89 | 88.16 | 71.84 | 68.21 | 79.03 | 80.32 | 75.71 | 80.37 |
Point RCNN * (Ours) | ReR50-ReFPN | 88.58 | 88.44 | 73.03 | 70.10 | 79.26 | 79.02 | 77.15 | 80.71 |
Point RCNN * (Ours) | Swin-T-FPN | 87.22 | 88.23 | 68.85 | 71.48 | 82.09 | 83.60 | 76.08 | 81.32 |
Method | Backbone | PL | BD | BR | GTF | SV | LV | SH | TC | |
---|---|---|---|---|---|---|---|---|---|---|
RetinaNet-O [5] | R50-FPN | 71.43 | 77.64 | 42.12 | 64.65 | 44.53 | 56.79 | 73.31 | 90.84 | |
FR-O [6] | R50-FPN | 71.89 | 74.47 | 44.45 | 59.87 | 51.28 | 68.98 | 79.37 | 90.78 | |
Mask RCNN [2] | R50-FPN | 76.84 | 73.51 | 49.90 | 57.80 | 51.31 | 71.34 | 79.75 | 90.46 | |
HTC [67] | R50-FPN | 77.80 | 73.67 | 51.40 | 63.99 | 51.54 | 73.31 | 80.31 | 90.48 | |
OWSR * [68] | R101-FPN | - | - | - | - | - | - | - | - | |
Oriented RCNN * [16] | R101-FPN | 87.20 | 84.67 | 60.13 | 80.79 | 67.51 | 81.63 | 89.74 | 90.88 | |
ReDet * [15] | ReR50-ReFPN | 88.51 | 86.45 | 61.23 | 81.20 | 67.60 | 83.65 | 90.00 | 90.86 | |
ReDet * [15] | Swin-T-FPN | 80.90 | 85.13 | 60.61 | 80.83 | 67.07 | 83.32 | 89.80 | 90.79 | |
Point RCNN * (Ours) | ReR50-ReFPN | 83.40 | 86.59 | 60.76 | 80.25 | 79.92 | 83.37 | 90.04 | 90.86 | |
Point RCNN * (Ours) | ReR50-ReFPN | 83.12 | 86.55 | 60.84 | 82.43 | 80.60 | 83.39 | 90.01 | 90.88 | |
Point RCNN * (Ours) | Swin-T-FPN | 83.88 | 85.22 | 60.76 | 79.40 | 81.64 | 83.48 | 89.98 | 90.75 | |
Point RCNN * (Ours) | Swin-T-FPN | 86.93 | 85.79 | 59.52 | 80.42 | 81.91 | 81.92 | 89.95 | 90.35 | |
BC | ST | SBF | RA | HA | SP | HC | CC | mAP | ||
RetinaNet-O [5] | R50-FPN | 76.02 | 59.96 | 46.95 | 69.24 | 59.65 | 64.52 | 48.06 | 0.83 | 59.16 |
FR-O [6] | R50-FPN | 77.38 | 67.50 | 47.75 | 69.72 | 61.22 | 65.28 | 60.47 | 1.54 | 62.00 |
Mask RCNN [2] | R50-FPN | 74.21 | 66.07 | 46.21 | 70.61 | 63.07 | 64.46 | 57.81 | 9.42 | 62.67 |
HTC [67] | R50-FPN | 75.12 | 67.34 | 48.51 | 70.63 | 64.84 | 64.48 | 55.87 | 5.15 | 63.40 |
OWSR * [68] | R101-FPN | - | - | - | - | - | - | - | - | 74.90 |
Oriented RCNN * [16] | R101-FPN | 82.21 | 78.51 | 70.98 | 78.63 | 79.46 | 75.40 | 75.71 | 39.69 | 76.45 |
ReDet * [15] | ReR50-ReFPN | 84.30 | 75.33 | 71.49 | 72.06 | 78.32 | 74.73 | 76.10 | 46.98 | 76.80 |
ReDet * [15] | Swin-T-FPN | 86.04 | 78.69 | 75.35 | 77.38 | 78.48 | 75.41 | 79.51 | 61.95 | 78.20 |
Point RCNN * (Ours) | ReR50-ReFPN | 87.45 | 84.50 | 72.79 | 77.32 | 78.29 | 77.48 | 78.92 | 47.97 | 78.74 |
Point RCNN * (Ours) | ReR50-ReFPN | 87.25 | 84.60 | 73.49 | 78.51 | 78.75 | 78.41 | 76.12 | 54.12 | 79.31 |
Point RCNN * (Ours) | Swin-T-FPN | 87.00 | 84.65 | 70.70 | 77.87 | 78.32 | 79.50 | 74.35 | 63.80 | 79.46 |
Point RCNN * (Ours) | Swin-T-FPN | 85.72 | 85.84 | 68.57 | 76.35 | 78.79 | 81.24 | 78.64 | 69.23 | 80.14 |
Method | Backbone | mAP (%) | mAP (%) |
---|---|---|---|
RC2 [69] | VGG16 | 75.70 | - |
RRPN [17] | R101 | 79.08 | 85.64 |
RPN [18] | VGG16 | 79.60 | - |
RRD [70] | VGG16 | 84.30 | - |
RoI-Trans. [13] | R101-FPN | 86.20 | - |
Gliding Vertex [26] | R101-FPN | 88.20 | - |
RDet [20] | R101-FPN | 89.26 | - |
DRN [64] | H34 | - | 92.7 |
CenterMap [65] | R50-FPN | - | 92.8 |
CSL [19] | R152-FPN | 89.62 | - |
SA-Net [21] | R101-FPN | 90.17 | 95.01 |
ReDet [15] | ReR50-ReFPN | 90.46 | 97.63 |
Orient RCNN [16] | R101-FPN | 90.50 | 97.60 |
Point RCNN (Ours) | ReR50-ReFPN | 90.53 | 98.53 |
Method | Backbone | Car | Airplane | mAP (%) |
---|---|---|---|---|
R-Yolov3 [71] | Darknet53 | 74.63 | 89.52 | 82.08 |
R-RetinaNet [5] | ResNet50 | 84.64 | 90.51 | 87.57 |
RoI-Trans. [13] | ResNet50 | 88.02 | 90.02 | 89.02 |
DAL [72] | ResNet50 | 89.25 | 90.49 | 89.87 |
SA-Net [21] | ResNet50 | 89.56 | 90.42 | 89.99 |
Point RCNN (Ours) | ReR50-ReFPN | 89.60 | 90.48 | 90.04 |
3.4. Ablation Study
3.4.1. Analysis of PointRPN
3.4.2. Effectiveness of PointReg
3.4.3. Analysis of Balanced Dataset Strategy
3.4.4. Factor-by-Factor Experiment
3.4.5. Visualization Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Source | Annotation | Categories | Instances | Images | Year |
---|---|---|---|---|---|---|
UCAS-AOD [60] | Google Earth | OBB | 2 | 14,596 | 1510 | 2015 |
HRSC2016 [59] | Google Earth | OBB | 1 | 2976 | 1061 | 2016 |
DOTA-v1.0 [6] | multi source | OBB | 14 | 188,282 | 2806 | 2018 |
DOTA-v1.5 | multi source | OBB | 15 | 402,089 | 2806 | 2019 |
Method | Recall (%) | Recall (%) | Recall (%) |
---|---|---|---|
PointRPN | 85.93 | 89.83 | 90.00 |
Regression Type of PointReg | mAP (%) |
---|---|
() | 77.25 |
() | 77.60 |
Oversampling Threshold () | mAP (%) |
---|---|
0 | 73.52 |
0.1 | 76.49 |
0.2 | 77.44 |
0.3 | 77.60 |
0.4 | 77.48 |
Method | PointRPN | Balanced Dataset Strategy | PointReg | mAP (%) |
---|---|---|---|---|
Baseline | 71.36 | |||
Point RCNN | ✓ | 74.17 | ||
✓ | 74.22 | |||
✓ | ✓ | 77.25 | ||
✓ | ✓ | ✓ | 77.60 |
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Zhou, Q.; Yu, C. Point RCNN: An Angle-Free Framework for Rotated Object Detection. Remote Sens. 2022, 14, 2605. https://doi.org/10.3390/rs14112605
Zhou Q, Yu C. Point RCNN: An Angle-Free Framework for Rotated Object Detection. Remote Sensing. 2022; 14(11):2605. https://doi.org/10.3390/rs14112605
Chicago/Turabian StyleZhou, Qiang, and Chaohui Yu. 2022. "Point RCNN: An Angle-Free Framework for Rotated Object Detection" Remote Sensing 14, no. 11: 2605. https://doi.org/10.3390/rs14112605