WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Authors: Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar and Vu N. Duong
Abstract: Incorporating deep-learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV’s ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 10.6% in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by floating-point operations per second (FLOPS). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency.
See AIDER Images Examples.png in the Figure folder for an illustration of some AIDER class images. See WATT-EffNet Structure FINAL.png in the Figure folder for an illustration of our algorithmic architecture design.
A version of our paper is available at arxiv: https://arxiv.org/pdf/2304.10811.pdf
(This repo may be updated. Stay tune.)
Our work is trained and evaluated on the Aerial Image Database for Emergency Response (AIDER) subset by Kyrkou and Theocharides [1]. The dataset comprised of images illustrating four major types of disasters: fire, floods, afermath of building collapses and traffic collisions, as well as images of non-disasters (normal class) in a relatively larger amount than the other four to replicate real-world scenario as close as possible. Some samples of the images of each class is shown in AIDER Images Examples.png. Unlike the original dataset which comprised of a total of 8540 images, the subset only contained 6433 images. The AIDER subset can be downloaded from https://zenodo.org/record/3888300.
AIDER subset image sets distribution in our approach:
| Class | Train | Valid | Test | Total per Class |
|---|---|---|---|---|
| Collapsed Building | 367 | 41 | 103 | 511 |
| Fire | 249 | 63 | 209 | 521 |
| Flood | 252 | 63 | 211 | 526 |
| Traffic | 232 | 59 | 194 | 485 |
| Normal | 2107 | 527 | 1756 | 4390 |
| Total per Set | 3207 | 753 | 2473 | 6433 |
Due to the imbalance class distribution of the AIDER subset, we utilized the mean F1 scores for the classification effectiveness evaluation and the floating point operations per second (FLOPs) for the efficiency computation.
We experimented with 1, 3 and 5 MBConv blocks in the EfficienNetB0 architecture, utilizing a width of 12 for 1MBConv block, widths of 2 to 7 for 3MBConv blocks and width factors of 2 and 3 for 5MBConv blocks. Possible combinations of our WATT-EffNet architecture can be written in the form WATT-EffNet-d-k, where d is the number of MBConv blocks and k is the width factor per block. Each variant is ran for 10 times during the evaluation and its mean F1 scores and corresponding standard deviation are recorded.
WATT-EffNet model variants:
| Model variant | F1 (%) | FLOPs | Parameters |
|---|---|---|---|
| WATT-EffNet-1-12 | 83.5 | 22 | 106,501 |
| WATT-EffNet-3-2 | 84.2 | 22 | 106,629 |
| WATT-EffNet-3-3 | 83.3 | 22 | 205,673 |
| WATT-EffNet-3-4 | 86.0 | 22 | 335,693 |
| WATT-EffNet-3-5 | 85.2 | 22 | 496,689 |
| WATT-EffNet-3-6 | 88.5 | 22 | 688,661 |
| WATT-EffNet-3-7 | 87.3 | 22 | 911,609 |
| WATT-EffNet-5-2 | 86.8 | 22 | 371,493 |
| WATT-EffNet-5-3 | 86.4 | 22 | 720,233 |
All the SOTA and our approach are trained from scratch on the AIDER subset and serves as a baseline. The SOTA evaluated include MobileNetV1 [2], MobileNetV2 [3], SqueezeNet [4], ShuffleNet [5], EfficientNetB0 [6] and EmergencyNet [2]. The optimal variant of our propsoed approach is the WATT-EffNet-3-6, which comprised of 3 MB Conv blocks and a width factor of 6.
| SOTA Model | F1 (%) | FLOPs | Parameters |
|---|---|---|---|
| MobileNetV1 [2] | 84.0 | 972 | 3,233,861 |
| MobileNetV2 [3] | 82.0 | 625 | 2,282,629 |
| SqueezeNet [4] | 87.3 | 531 | 725,073 |
| ShuffleNet [5] | 84.7 | 972 | 4,023,865 |
| EfficientNetB0 [6] | 80.0 | 774 | 3,499,453 |
| EmergencyNet [2] | 84.5 | 185 | 94,420 |
| WATT-EffNet-3-6 | 88.5 | 22 | 688,661 |
- Run Data_loading.py. This would load the AIDER data subset, sort the images and labels into the five respective classes, and split them into train, valid and test set.
- Due to the imbalance class nature of the subset, run Undersampling.py to perform undersampling of the classes so that the imbalance would be removed.
- Run one_hot_encode.py to convert the numerical labels into one-hot encoded label.
- Run Attention.py for the attention module.
- Run the MBConvblocks.py.
- Run the watt-effnet-3-6.py. Note that based on our experiment, the WATT-EffNet-3-6 is the ideal WATT-EffNet configuration. The attention is imbued in the architecture.
- Proceed to train the model.
- Finally, run the F1 metrics.py to output the F1 score, and the compute_flops.py to output the flops value.
Please cite the following paper if you find it useful for your work:
G. Y. Lee, T. Dam, M. M. Ferdaus, D. P. Poenar, and V. N. Duong, “Watt-EffNet: A lightweight and accurate model for classifying aerial disaster images,” IEEE Geoscience and Remote Sensing Letters, pp. 1–1, 2023 (In Press).
@article{lee2023watt, title={WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images}, author={Lee, Gao Yu and Dam, Tanmoy and Ferdaus, Md Meftahul and Poenar, Daniel Puiu and Duong, Vu N}, journal={IEEE Geoscience and Remote Sensing Letters}, year={2023}, publisher={IEEE} }
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