A real-time method for detecting Canada Goldenrod based on an improved YOLOv5 network
Bibliographic record
Abstract
In order to improve the detection efficiency of Canada goldenrod, we propose an improved YOLOv5 network. Firstly, a channel attention module is added to the YOLOv5 network, which enhances target detection accuracy while maintaining the lightweight nature of the model. Secondly, a Spatial-to-Depth Convolution (SPD-Conv) layer has been integrated into the backbone network to enhance its capability to recognize low-resolution images and small targets. Finally, the substitution of the SiLU (Sigmoid Linear Unit) activation function in the initial network has been executed with a meta-Acon (Activate or Not) adaptive activation function. This adjustment serves to enhance the network’s generalization prowess and facilitates swifter convergence. Empirical findings indicate that the mean Average Precision (mAP) for the enhanced YOLOv5 network attains 65.5%, manifesting a 5.8% augmentation relative to the original configuration. The enhanced network leads to faster convergence and higher detection accuracy, better meeting the needs of invasive Canada goldenrod detection and control applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".