Research on a natural scene Korla pear detection method based on ECA and BiFPN improved YoloV11
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".