MIS-YOLOv8: An Improved Algorithm for Detecting Small Objects in UAV Aerial Photography Based on YOLOv8
Why this work is in the frame
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Bibliographic record
Abstract
Small objects’ detection from a drone’s perspective has always been a challenging issue in the field of object detection. To address the problems of low recognition accuracy and information loss in small object detection, this article proposed MIS-YOLOv8 algorithm, primarily aimed at resolving the issue of small object loss during the detection process of the classic YOLOv8s algorithm. First, a multilevel feature extraction (MFE) module was designed for enriching the feature representation capabilities capable of extracting objects from different scales. Second, a small object detection mechanism was incorporated for improving the detection ability. Finally, the integration of depthwise atrous flexible convolutions is introduced, enabling a rich capture of information from spatial to depth dimensions, thereby reducing the loss of small objects. The improved MIS-YOLOv8 algorithm validation was conducted on the VisDrone2019 dataset, where MIS-YOLOv8 demonstrated a 9% and 6.2% increase in mAP@0.5 and mAP@0.5:0.95, respectively, compared with YOLOv8s. The experimental results indicated that the improved model exhibits superior performance in small object detection for drones.
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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 | 0.000 | 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 it