Object Detection Using Convolutional Neural Networks for Natural Disaster Recovery
Bibliographic record
Abstract
Natural disasters cause a great damage to human life. As these disasters occur naturally, no one can able to stop their occurrences. But for recovery there is a team named Disaster management or emergency management which helps in recovery of human loss. As recovering and analyzing the objects is not easy, it will be a tough challenge for Disaster management team to identify and process large amount of data in real-time. To make this simple and easy Convolutional Neural Networks (CNN) models are used for object detection of disaster's aftermath. As there are various types of natural disasters such as hurricanes, tsunamis, floods, earthquakes etc., this study focuses on floods and earthquake images for object detection by using neural networks which has the ability to recognize objects easily. The network is processed on the DISASTER dataset which contains 2423 images out of which 1073 images belong to Flood and 1350 images belong to Earthquake. In this study ResNet50, VGG-16 and VGG-19 pre-trained models are used. These pretrained models are CNN models which have been already trained on some sort of data. By using pre-trained models it will be more easy for object detection of flood and earthquake images. Among the three pre-trained models VGG-19 gets highest accuracy of 94.22%. As this study focused on floods and earthquake images for object detection. In future, by using different dataset and different images object detection will be done which will be helpful for recovery of human loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".