Optimal Squeeze Net with Deep Neural Network-Based Arial Image Classification Model in Unmanned Aerial Vehicles
Bibliographic record
Abstract
In present times, unmanned aerial vehicles (UAVs) are widely employed in several real time applications due to their autonomous, inexpensive, and compact nature. Aerial image classification in UAVs has gained significant interest in surveillance systems that assist object detection and tracking processes. The advent of deep learning (DL) models paves a way to design effective aerial image classification techniques in UAV networks. In this view, this paper presents a novel optimal Squeezenet with a deep neural network (OSQN-DNN) model for aerial image classification in UAV networks. The proposed OSQN-DNN model initially enables the UAVs to capture images using the inbuilt imaging sensors. Besides, the OSQN model is applied as a feature extractor to derive a useful set of feature vectors where the coyote optimization algorithm (COA) is employed to optimally choose the hyperparameters involved in the classical SqueezeNet model. Moreover, the DNN model is utilized as a classifier that aims to allocate proper class labels to the applied input aerial images. Furthermore, the usage of COA for hyperparameter tuning of the SqueezeNet model helps to considerably boost the overall classification performance. For examining the enhanced aerial image classification performance of the OSQN-DNN model, a series of experiments were performed on the benchmark UCM dataset. The experimental results pointed out that the OSQN-DNN model has resulted in a maximum accuracy of 98.97% and a minimum running time of 1.26mts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".