Single-Species Leaf Detection against Complex Backgrounds with YOLOv5s
Bibliographic record
Abstract
Accurate and rapid localization and identification of tree leaves are of significant importance for urban forest planning and environmental protection. Existing object detection neural networks are complex and often large, which hinders their deployment on mobile devices and compromises their efficiency in detecting plant leaves, especially against complex backgrounds. To address this issue, we collected eight common types of tree leaves against complex urban backgrounds to create a single-species leaf dataset. Each image in this dataset contains only one type of tree but may include multiple leaves. These leaves share similar shapes and textures and resemble various real-world background colors, making them difficult to distinguish and accurately identify, thereby posing challenges to model precision in localization and recognition. We propose a lightweight single-species leaf detection model, SinL-YOLOv5, which is only 15.7 MB. First, we integrated an SE module into the backbone to adaptively adjust the channel weights of feature maps, enhancing the expression of critical features such as the contours and textures of the leaves. Then, we developed an adaptive weighted bi-directional feature pyramid network, SE-BiFPN, utilizing the SE module within the backbone. This approach enhances the information transfer capabilities between the deep semantic features and shallow contour texture features of the network, thereby accelerating detection speed and improving detection accuracy. Finally, to enhance model stability during learning, we introduced an angle cost-based bounding box regression loss function (SIoU), which integrates directional information between ground-truth boxes and predicted boxes. This allows for more effective learning of the positioning and size of leaf edges and enhances the model’s accuracy in detecting leaf locations. We validated the improved model on the single-species leaf dataset. The results showed that compared to YOLOv5s, SinL-YOLOv5 exhibited a notable performance improvement. Specifically, SinL-YOLOv5 achieved an increase of nearly 4.7 percentage points in the mAP@0.5 and processed an additional 20 frames per second. These enhancements significantly enhanced both the accuracy and speed of localization and recognition. With this improved model, we achieved accurate and rapid detection of eight common types of single-species tree leaves against complex urban backgrounds, providing technical support for urban forest surveys, urban forestry planning, and urban environmental conservation.
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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 | 0.000 | 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".