Pedestrian detection in aerial image based on convolutional neural network with attention mechanism and multi-scale prediction
Bibliographic record
Abstract
Pedestrian object detection is crucial in intelligent systems such as traffic management and surveillance. Traditional machine learning methods have shown drawbacks, including low accuracy and slow processing. Convolutional Neural Network (CNN)-based algorithms have achieved notable progress, but mainstream CNNs still struggle with slow speed and low accuracy, particularly for small and occluded targets from aerial perspectives. In this paper, we propose a Multi-Scale Attention YOLO (MSA-YOLO) algorithm to address these issues. MSA-YOLO incorporates a Squeeze, Excitation, and Cross Stage Partial (SECSP) channel attention module to extract richer pedestrian features with minimal additional parameters. A multi-scale prediction module is also introduced to capture information across different scales, improving small object detection and reducing missed detections. To evaluate our approach, we manually collect and annotate the Aerial Pedestrian dataset (AP dataset), which, to our knowledge, provides more annotations, varied scenes, and diverse view angles than comparable existing datasets. The high-resolution images in the AP dataset allow for capturing more detailed pedestrian features, which can enhance model performance. Experimental results show that, on the AP dataset, MSA-YOLO demonstrates clear advantages over several widely used object detection and pedestrian detection models developed in recent years, indicating its potential dual benefits in terms of accuracy and efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".