Tsetse Fly Detection and Sex Classification Model Enrichment Employing <scp>YOLOv8</scp> and <scp>YOLO11</scp> Architecture
Bibliographic record
Abstract
ABSTRACT The sterile insect technique (SIT) represents a highly effective and promising method for combating tsetse fly‐related infections, which involves the release of sterilized male tsetse flies in the assigned zones. However, tsetse fly rearing poses specific challenges, particularly in the tsetse sex separation, as this process is labor‐intensive and incurs significant costs. Here, we report a simple model that classifies tsetse flies by sex using an object detection model based on the YOLO algorithm. This paper also conducted a comparative analysis of YOLOv8 and YOLO11 deep learning models, focusing on their efficacy in tsetse fly detection and classification using a range of performance metrics and statistical analysis. The findings reveal that the classification accuracy of YOLO11 stands at 97.6%, whereas YOLOv8 achieves 95.6%. The classification precision of YOLO11 in identifying tsetse flies is 88.6%, while that of YOLOv8 is 85.9%. Additionally, YOLO11 demonstrates an inference speed of 13.0 ms, slightly faster than YOLOv8's 13.4 ms in tsetse sex detection. Moreover, YOLO11 outperformed YOLOv8 in both F1 score and mAP@0.5–0.9, a success attributed to its enhanced architectural design. However, statistical tests indicate there is no significant difference between the two models, achieving p values ≥ 0.05 for all metrics. This study adds value to tsetse rearing and fly‐based disease control by offering automated tsetse sex detection insights into its practical uses in real‐world contexts. Furthermore, this research enriches the understanding of the two models with tsetse flies as the focal point and recommends a more effective and accurate detection approach. Finally, integrating the model with the mobile object detection Android app will reduce tsetse sex sorting dependency on experienced technical experts and enhance tsetse rearing productivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".