Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health
Bibliographic record
Abstract
The occurrence of heated and immature canola kernels caused by excessive drying and frost damage is undesired by grain buyers due to low oil yield and diminished market value. The current grading process is visually examining each kernel's endosperm colour and counting the damaged seeds. As this process is time-consuming, the current study proposes an automated grading technique based on multi-object detection, tracking, and counting. The detection task was achieved via an improved YOLOv7 network (YOLOv7_ours) to increase its performance in accurately identifying small objects and decrease its computational cost and size; by adding two convolutional block attention modules and substituting convolutional layers with ghost layers in all the Efficient Layer Aggregation Networks modules, and in the Spatial Pyramid Pooling Cross Stage Partial module present in YOLOv7. The detection weights were fed to the ByteTrack multiple object tracker to track the detections frame by frame in a video feed. The unique identities generated by the tracker for each detected object of interest were then used to count the number of defects using a line cross algorithm. The mean average precision ([email protected]) obtained after training the YOLOv7_ours model was 1.02% better and its cost and size were 32.1% and 37.1% lower than the baseline YOLOv7 model. In a test video, the tracking model achieved a multi-object tracking accuracy of 84.8% and the counting accuracy was determined to be 93.9%. This three-stage model can be readily deployed in an edge device for accurate and real-time grading of canola kernels by grain buyers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".