Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images
Abstract
:1. Introduction
- (1)
- To enhance the representation of feature maps and focus on the region of interest of oil tanks, the Transformer encoder is integrated into the YOLOX-TR, which can improve the localization accuracy of oil tanks in high-density areas.
- (2)
- To augment the extraction of discriminative features between the two types of multiscale oil tanks, YOLOX-TR employs structural reparameterized VGG-like (RepVGG) blocks to reparameterize the backbone with multi-branch typologies without increasing computation in inference time, which can help distinguish the two types of tanks and improve the classification accuracy.
- (3)
- To realize end-to-end detection in large-scale SAR images automatically, a slicing detection module based on sliding window detection and non maximum suppression (NMS) is employed to the detect layer of YOLOX-TR, which facilitates the deployment of the model in practical applications.
2. Materials and Methods
2.1. Dataset Construction
2.2. Construction of the YOLOX-TR Model
2.2.1. Overview of YOLOX-TR
2.2.2. Transformer Encoder
2.2.3. Reparameterized Backbone RepCSP
3. Experiments and Results
3.1. Dataset and Setting
3.2. Implementation Details
3.3. Evaluation Metric
3.4. Ablation Experiments
3.5. Comparison with Other Detectors
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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RepCSP | Transformer | mAP% | mAP0.5% | Precision% | Recall% | F1% | GFLOPS |
---|---|---|---|---|---|---|---|
× | × | 58.35 | 92.12 | 92.18 | 89.22 | 90.68 | 26.6 |
√ | × | 59.53 | 93.78 | 93.85 | 90.61 | 92.20 | 25.3 |
× | √ | 59.96 | 94.13 | 94.97 | 90.89 | 92.89 | 27.3 |
√ | √ | 60.80 | 94.82 | 95.64 | 91.91 | 93.74 | 26.1 |
RepCSP | Transformer | AP0.5% | AP0.5:0.95% | ||
---|---|---|---|---|---|
Floating | Fixed | Floating | Fixed | ||
× | × | 96.4 | 87.8 | 68.3 | 48.4 |
√ | × | 97.2 | 90.4 | 68.7 | 50.4 |
× | √ | 97.4 | 90.9 | 68.6 | 51.1 |
√ | √ | 97.7 | 91.9 | 69.1 | 52.5 |
Backbone | Neck | mAP% | mAP0.5% | Parameters (M) | GFLOPS |
---|---|---|---|---|---|
× | × | 58.35 | 92.12 | 8.94 | 26.6 |
√ | × | 59.20 | 93.81 | 10.12 | 27.0 |
√ | √ | 59.96 | 94.13 | 11.30 | 27.3 |
Method | Roof Type (AP0.5%) | mAP0.5% | GFLOPS | Parameters (M) | |
---|---|---|---|---|---|
Floating | Fixed | ||||
RetinaNet | 89.3 | 72.1 | 80.7 | 81.87 | 36.13 |
Faster RCNN | 90.3 | 78.5 | 84.4 | 91.01 | 41.13 |
SSD(300) | 90.0 | 79.9 | 85.0 | 137.31 | 23.88 |
Yolov5-s | 95.2 | 88.4 | 91.8 | 15.9 | 7.3 |
Yolox-TR | 97.7 | 91.9 | 94.8 | 26.1 | 8.48 |
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Wu, Q.; Zhang, B.; Xu, C.; Zhang, H.; Wang, C. Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images. Remote Sens. 2022, 14, 3246. https://doi.org/10.3390/rs14143246
Wu Q, Zhang B, Xu C, Zhang H, Wang C. Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images. Remote Sensing. 2022; 14(14):3246. https://doi.org/10.3390/rs14143246
Chicago/Turabian StyleWu, Qian, Bo Zhang, Changgui Xu, Hong Zhang, and Chao Wang. 2022. "Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images" Remote Sensing 14, no. 14: 3246. https://doi.org/10.3390/rs14143246