Military Image Captioning for Low-Altitude UAV or UGV Perspectives
Abstract
:1. Introduction
- Dataset scarcity: There is no established benchmark dataset in the field of MilitIC. The datasets from military target detection and camouflaged detection are not readily applicable to this field.
- Deficiency of a specific method: there is a scarcity of methods specifically proposed for MilitIC tasks that can effectively highlight military semantics in caption generation.
- We created the first open-access MilitIC benchmark dataset, which was named the Military Objects Dataset in Real Combat (MOCO) and not only includes real and complex military scenes but also provides a vast number of rich captions. MOCO is available at https://github.com/Panlizhi/MOCO (accessed on 1 August 2024).
- We propose a novel encoder–augmentation–decoder architecture, MAE-MilitIC, to leverage image–text modalities to guide caption generation, and introduce the MAE mechanism to augment the image embeddings with explicit military text in the semantic subspace.
- Extensive qualitative and quantitative experiments on two challenging datasets demonstrated that the proposed MAE-MilitIC outperformed existing state-of-the-art image-captioning models.
2. Related Works
2.1. Image Captioning
2.2. Military Image Captioning (MilitIC)
2.3. Military Image Dataset
3. MOCO: A Military Objects Dataset in Real Combat for Military Image Captioning
4. MAE-MilitIC: Map Augmentation Embedding to Enhance Semantics for Military Image Captioning
4.1. Backbone of MAE-MilitIC
4.2. Map Augmentation Embedding (MAE)
4.2.1. Semantic Subspace
4.2.2. Mapping Step
4.2.3. Augmentation Step
5. Experiments and Results
5.1. Evaluation Metrics
5.2. Dataset Settings
5.3. Experimental Settings
5.4. Experimental Results
5.5. Ablation Studies
5.5.1. Parameter Optimization
5.5.2. Ablation of MAE-MilitIC
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
DURC Statement
Conflicts of Interest
Appendix A
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Dataset | Categories | Number of Captions | Task | Open Access |
---|---|---|---|---|
GMTD | Tank, transporter, and rocket launcher | 11,036 images, 20,132 targets | Object detection for ground equipment | - |
SFD | Pistol and rifle | 3140 images, 3140 labels | Object detection for guns | - |
COD10K+ | Person | 2592 images, 2592 labels | Object detection for camouflage | - |
MHCD | Person, airplane, vehicle, and warship | 3000 images, 3000 labels | Object detection for camouflage | Yes |
MADD | Aircraft | 14,088 images, 14,088 labels | Object detection for aircrafts | Yes |
MOCO (ours) | Person, tank, rocket, fighter jet, helicopter, AV, UGV, boat, and defensive structure | 7449 images, 37,245 captions | Image captioning for real combat | Yes |
Text Prompts | Main Elements | ||
---|---|---|---|
Military Personnel | Combat Equipment | Battlefield Environment | |
Soldiers crossed the frontline under heavy fire. | Soldier | Fire | |
Snipers positioned on rooftops monitored enemy movements. | Sniper | Rooftop | |
Unmanned aerial drones conducted reconnaissance missions. | Unmanned aerial drones | ||
UGVs transported supplies to forward positions. | UGV | ||
Fighter jets engaged in combat maneuvers above the foggy mountains. | Fighter jet | Foggy mountains | |
The jet conducted a reconnaissance mission under the cover of darkness. | Jet | Darkness | |
The tank engaged enemy forces in the open plains. | Tank | Open plains | |
The military vehicle traversed the muddy battlefield under a heavy downpour. | Military vehicle | Heavy downpour | |
The soldiers that operated the anti-aircraft unit targeted enemy aircraft in the sky. | Soldier | Anti-aircraft unit | Sky |
Armored vehicles deployed smoke screens to conceal troop movements. | Armored vehicle | Smoke screens | |
The howitzer launched rounds into the night sky. | Howitzer | Night sky | |
Cannons positioned on the coastal cliffs targeted ships at sea. | Cannon | Sea | |
The combat engineers are setting up fences and trenches. | Combat engineer | Trench | |
Engineering vehicles repaired damaged roadway. | Engineering vehicle | Roadway |
Method | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | ROUGE-L | SPICE |
---|---|---|---|---|---|---|---|
ClipCap | 0.441 | 0.275 | 0.173 | 0.111 | 0.158 | 0.337 | 0.102 |
CapDec | 0.363 | 0.184 | 0.086 | 0.041 | 0.111 | 0.264 | 0.059 |
MAE-MilitICGSO | 0.494 | 0.322 | 0.212 | 0.141 | 0.163 | 0.353 | 0.113 |
MAE-MilitICPCA | 0.460 | 0.290 | 0.181 | 0.112 | 0.158 | 0.336 | 0.102 |
Method | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | ROUGE-L | SPICE |
---|---|---|---|---|---|---|---|
ClipCap | 0.547 | 0.443 | 0.369 | 0.271 | 0.230 | 0.576 | 0.437 |
CapDec | 0.542 | 0.445 | 0.370 | 0.265 | 0.241 | 0.586 | 0.430 |
MAE-MilitICGSO | 0.553 | 0.452 | 0.379 | 0.282 | 0.236 | 0.583 | 0.449 |
MAE-MilitICPCA | 0.555 | 0.448 | 0.371 | 0.269 | 0.241 | 0.578 | 0.459 |
Methods | Mapping | Augmentation | Residual | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | ROUGE-L | SPICE |
---|---|---|---|---|---|---|---|---|---|---|
MAE-MilitICGSO | ✓ | ✗ | ✗ | 0.335 | 0.183 | 0.102 | 0.057 | 0.115 | 0.279 | 0.068 |
✗ | ✓ | ✗ | 0.440 | 0.276 | 0.174 | 0.112 | 0.160 | 0.331 | 0.106 | |
✓ | ✓ | ✗ | 0.475 | 0.303 | 0.193 | 0.124 | 0.156 | 0.346 | 0.107 | |
✓ | ✓ | ✓ | 0.494 | 0.322 | 0.212 | 0.141 | 0.163 | 0.353 | 0.113 | |
MAE-MilitICPCA | ✓ | ✗ | ✗ | 0.396 | 0.220 | 0.123 | 0.073 | 0.127 | 0.299 | 0.076 |
✗ | ✓ | ✗ | 0.440 | 0.276 | 0.174 | 0.112 | 0.160 | 0.331 | 0.106 | |
✓ | ✓ | ✗ | 0.453 | 0.278 | 0.172 | 0.105 | 0.150 | 0.325 | 0.100 | |
✓ | ✓ | ✓ | 0.460 | 0.290 | 0.181 | 0.112 | 0.158 | 0.336 | 0.102 |
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Pan, L.; Song, C.; Gan, X.; Xu, K.; Xie, Y. Military Image Captioning for Low-Altitude UAV or UGV Perspectives. Drones 2024, 8, 421. https://doi.org/10.3390/drones8090421
Pan L, Song C, Gan X, Xu K, Xie Y. Military Image Captioning for Low-Altitude UAV or UGV Perspectives. Drones. 2024; 8(9):421. https://doi.org/10.3390/drones8090421
Chicago/Turabian StylePan, Lizhi, Chengtian Song, Xiaozheng Gan, Keyu Xu, and Yue Xie. 2024. "Military Image Captioning for Low-Altitude UAV or UGV Perspectives" Drones 8, no. 9: 421. https://doi.org/10.3390/drones8090421