An improved lightweight irrigation canal segmentation network with direction perception for agricultural UAVs
Bibliographic record
Abstract
The inspection for irrigation canals based on unmanned aerial vehicles (UAVs) is an important and challenging task in the field of modern agriculture. Specifically, accurate segmentation of irrigation canals from UAV images faces several challenges such as complex background textures, vegetation occlusions, and varying lighting conditions, which can lead to blurred canal boundaries and discontinuous features. To improve the accuracy and robustness of the image segmentation, an improved lightweight semantic segmentation network (named GEA-UNet) is proposed in this paper. In the proposed model, a direction perception attention module is presented to enhance orientation sensitivity. In addition, an edge detection auxiliary module is designed for refined boundary learning, and a context-aware segmentation module is proposed to capture local and global features of the irrigation canals. Evaluation results on the self-constructed irrigation canal dataset show that the proposed GEA-UNet model achieves an accuracy of 98.9%, mean Intersection over Union of 85.4%, and F1-score of 92.2%, outperforming other mainstream semantic segmentation models. Path extraction experiments using sliding projection and RANSAC regression further showed that the proposed method reduces the average angular error to 1.27 \(^{\circ }\) and the average fitting time to 3.67 ms, significantly enhancing the navigation accuracy and efficiency for agricultural UAVs. This work provides an effective and efficient solution for autonomous UAV-based canal inspection, contributing to intelligent decision-making and precision management in modern irrigation systems.
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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 | 0.000 |
| 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".