Pest-PVT: A model for multi-class and dense pest detection and counting in field-scale environments
Bibliographic record
Abstract
Field-scale pest monitoring is crucial for evaluating insect infestations in agricultural environments. The Pest24 dataset presents unique challenges due to the small pest target sizes, high target similarity, and dense pest distribution. To address these challenges, we propose Pest-PVT, a comprehensive framework based on the Pyramid Vision Transformer v2 (PVTv2) network model. Pest-PVT adopts an anchor-free approach using Fully Convolutional One-Stage Object Detection (FCOS) to enhance small object detection and incorporates Adaptive Training Sample Selection (ATSS) to mitigate sample imbalance bias. Dynamic Heads (DyHead) are employed to handle pests of varying scales and spatial changes, while Shunted Self-Attention (SSA) enhances multi-scale feature capture and reduces memory consumption. Compared to 23 other mainstream detection models and previously published works, our proposed Pest-PVT outperforms state-of-the-art detectors on pest detection datasets, achieving the outperformed scores in detection evaluation metrics such as mAP, Precision, Recall, and F1-Score, reaching 77.2 %, 78.42 %, 81.27 %, and 0.80, respectively. With a parameter size of only 24.74 M, Pest-PVT is suitable for integration into edge devices with limited resources. This work represents a significant contribution to the field of field-scale pest monitoring and addresses the specific challenges posed by the Pest24 dataset. Our code is made available at https://github.com/jlauwcj/pest-pvt .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".