Detection of multi-size peach in orchard using RGB-D camera combined with an improved DEtection Transformer model
Bibliographic record
Abstract
The first major contribution of the paper is the proposal of using an improved DEtection Transformer network (named R2N-DETR) and Kinect-V2 camera for detecting multiple-size peaches under orchards with varied illumination and fruit occlusion. R2N-DETR model first employed Res2Net-50 to extract a fused low-high level feature map containing fine spatial features and precise semantic information of multi-size peaches from Red-Green-Blue-Depth (RGB-D) images. Second, the encoder-decoder was performed on the feature map to obtain the global context. Finally, all detected objects were detected according to each object’s global context. For the detection of 1101 RGB-D images (imaged from two orchards over three years), the R2N-DETR model achieves an average precision of 0.944 and an average detecting time of 53 ms for each image. The developed system could provide precise visual guidance for robotic picking and contribute to improving yield prediction by providing accurate fruit counting.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".