Research on a natural scene Korla pear detection method based on ECA and BiFPN improved YoloV11
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the key technical challenges of complex background noise interference, fruit mutual occlusion, and multi-scale object recognition in natural scene Korla pear detection tasks, an improved YoloV11 object detection algorithm integrating the Efficient Channel Attention (ECA) mechanism and Bidirectional Feature Pyramid Network (BiFPN) is proposed (ECABiFPN-YOLOv11). By introducing the ECA module to adaptively optimize feature channel weights and combining the BiFPN architecture to achieve efficient cross-level feature fusion, the model’s perception and expression capabilities for multi-scale features of Korla pear objects are significantly enhanced. The experimental results show that the improved model reaches 86.8% on the mean average precision (mAP50) index, which is 4.7 percentage points higher than that of the original YoloV11 (82.1%). The mAP@0.5:0.95 value is 62.7%, which is 4.4% higher than that of the original model. The training box_loss (final) value is 3.7% lower than that of the original model, and the verification box_loss (final) value is 3.6% lower than that of the original model. These results provide reliable technical support for the research and development of automatic grading and sorting of fragrant pear fruits and intelligent picking systems in the field of smart agriculture.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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