PRO-YOLOv4-tiny: towards more balance between accuracy and speed in the detection of small targets in remotely sensed images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aiming at the problem of low accuracy of small target detection in Unmanned Aerial Vehicle (UAV) aerial remote sensing images and limited computing resources of UAV platform, this paper proposes a novel real-time detection algorithm for aerial remote sensing images. Firstly, the Spatial Pyramid Pooling-Fast (SPPF) is used to fuse the global and local features in different receptive fields. Second, we propose the Drone-captured Path Aggregation Network (CPAN) to enrich the semantic features of small targets while keeping the model lightweight. CPAN adds a new detection layer and uses the fusion of deep and shallow feature information to enhance the detection of small targets. At the same time, it uses depthwise separable convolution (DSC) to reduce the number of parameters. Then, Coordinate Attention (CA) is used to capture the cross-channel information with direction-aware and position-aware information. Finally, Decoupled-Head is introduced to make the detection of classification and coordinate regression more robust. We evaluate our model based on the aerial remote sensing dataset. The experimental results show that the proposed method provides a better balance between accuracy and speed than other lightweight networks.
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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 | 0.001 | 0.001 |
| 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