Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery

Ik Jae Lee; Hieu D. Nguyen; Mahbubur Meenar; Carlos Morrison Martinez; Cameron Connelly

cs.CVSep 18, 2026

Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery

Authors: Ik Jae Lee, Hieu D. Nguyen, Mahbubur Meenar, Carlos Morrison Martinez, Cameron Connelly

Organizations: Department of Mathematics, Rowan University · Department of Geography, Planning, and Sustainability, Rowan University · Department of Computer Science, Rowan University

Abstract

Reliable plant-level information from unmanned aerial vehicle (UAV) imagery is important for automated crop monitoring. However, in dense crop canopies, adjacent plants frequently overlap and are detected as a single object, reducing the reliability of plant-level measurements. This study presents a geometry-aware post-detection framework for resolving overlapping plant instances using standard RGB UAV imagery. The framework combines object detection with geometric clustering of plant components. Leaves or branches detected within each bush-level region are represented using two complementary geometric features: component centroids and radial intersection points (RIPs) derived from detected plant structures. K-means and Gaussian mixture models determine whether a detected region contains a single plant or two overlapping plants. Density filtering suppresses spurious radial intersections, and a post-pipeline ensemble combines spatial and directional geometric information. The framework was evaluated using UAV imagery of eggplant and tomato crops under field conditions. Centroid-based clustering achieved an F1-score of 0.89 for eggplant, while the combined centroid-RIP approach achieved the best tomato performance, with an accuracy of 0.80, precision of 1.00, and F1-score of 0.75 using K-means. Density filtering substantially improved RIP-based clustering for tomato. The proposed approach provides a lightweight, modular engineering solution that can be integrated with existing RGB UAV monitoring pipelines without additional depth sensors, pixel-level segmentation, three-dimensional reconstruction, or retraining of the primary bush detector. The results demonstrate that geometric reasoning applied to existing detector outputs can complement deep-learning-based object detection and improve plant-level interpretation in dense agricultural canopies.

Figures & tables

Figure 1: Two adjacent tomato plants mis-detected as a single plant (red bounding box) due to canopy overlap.

Panel of Figure 2 — Figure 2: Schematic illustration of approximately radial leaf or branch structure in eggplant and tomato plants. Illustrative images were generated using ChatGPT (OpenAI, March 2026) and reviewed by the authors for accuracy.

Vegetable	Number of Images	Number of Bush Annotations
Eggplant	10	111
Tomato	20	138

Table 1: Bush datasets used for training the object detection model.

Panel of Figure 3 — Figure 3: Examples of bush-level bounding-box annotations for eggplant and tomato.

Vegetable	Number of Images	Number of Annotations
Eggplant	14	5336 (leaf)
Tomato	10	457 (branch)

Table 2: Leaf/branch datasets used for fine-scale component detection.

Panel of Figure 4 — Figure 4: Examples of fine-grained plant-part annotations (leaf/branch).

Crop	Class	Tuning set	Validation set
Eggplant	1-in-1	30	10
Eggplant	2-in-1	30	10
Tomato	1-in-1	15	10
Tomato	2-in-1	15	10

Table 3: Bush-level dataset split used for tuning and evaluation of the clustering pipeline.

Vegetable	Precision	Recall	mAP50
Eggplant	0.93728	0.93411	0.96104
Tomato	0.92753	0.8125	0.85404

Table 4: Bush model accuracy results.

Vegetable	Precision	Recall	mAP50
Eggplant (Leaf)	0.91406	0.60297	0.72498
Tomato (Branch)	0.69936	0.75	0.73945

Table 5: Leaf/branch model accuracy results.

Panel of Figure 5 — Figure 5: Representative 2-in-1 misdetection cases from the bush detector. Each ROI contains two plants but is initially detected as a single bush box, motivating bush-level clustering for post-processing.

Panel of Figure 6 — Figure 6: Centroid-based representation for a single bush ROI (yellow), showing leaf detections inside the ROI (blue boxes) and their computed centroids (red dots).

Panel of Figure 7 — Figure 7: Bounding box misdetection of two overlapping plants.

Panel of Figure 8 — Figure 8: Radial intersection points (RIP).

Panel of Figure 9 — Figure 9: Leaf centroid versus radial intersection point (RIP) clustering using K-means.

Panel of Figure 10 — Figure 10: RIP density thresholding.

Figure 11: Block diagram of the base bush-level clustering pipeline. The same steps are applied independently to each bush ROI using either centroid-based or RIP-based feature points, followed by clustering with K-means or GMM and silhouette-based classification into 1-in-1 or 2-in-1 cases.

Panel of Figure 12 — Figure 12: Example of the RIP-based processing pipeline on real drone imagery. (a) Radial line construction from detected leaf boxes. (b) Generation of raw intersection points. (c) Filtering of intersection points based on ROI membership. (d) Clustering of retained points into two groups. Cluster centroids are indicated by $\times$ symbols.

Panel of Figure 13 — Figure 13: Example of minimum-density filtering applied to intersection points. Raw intersection points are first restricted to the ROI and then aggregated into a density map, low-density cells are removed, and the remaining points are retained for downstream clustering.

Metric	K-means	GMM
Accuracy	0.90000	0.90000
Precision (2-in-1)	1.00000	1.00000
Recall (2-in-1)	0.80000	0.80000
F1-score (2-in-1)	0.88889	0.88889

Table 6: Eggplant centroid-based clustering results on the validation set without density filtering.

Panel of Figure 14 — Figure 14: Representative eggplant centroid-based clustering examples using K-means on ground-truth 2-in-1 ROIs. The left panel shows a correctly resolved case, while the right panel shows a missed 2-in-1 case that is incorrectly classified as 1-in-1 because its silhouette score falls below the decision threshold. Cluster centroids are indicated by $\times$ .

Panel of Figure 15 — Figure 15: Eggplant centroid-based borderline failure case. The left panel shows the actual centroid-based pipeline output for a bush ROI labeled as 2-in-1 but predicted as 1-in-1. The right panel shows the same ROI under a forced two-cluster fit ( $k=2$ ), included for diagnostic comparison. Cluster centroids are marked by $\times$ .

Figure 16: Representative eggplant true-negative example for centroid-based clustering using K-means. The ground-truth label is 1-in-1, and the centroid-based pipeline correctly predicts 1-in-1. Silhouette score: 0.361. Cluster centroid is indicated by $\times$ .

Metric	K-means	GMM
Accuracy	0.75000	0.75000
Precision (2-in-1)	1.00000	1.00000
Recall (2-in-1)	0.50000	0.50000
F1-score (2-in-1)	0.66667	0.66667

Table 7: Tomato centroid-based clustering results on the validation set without density filtering.

Panel of Figure 17 — Figure 17: Representative tomato centroid-based clustering examples using K-means on ground-truth 2-in-1 ROIs. The left panel shows a correctly resolved case, while the right panel shows a missed 2-in-1 case that is incorrectly classified as 1-in-1 because its silhouette score falls below the decision threshold. Cluster centroids are indicated by $\times$ .

Metric	K-means	GMM
Accuracy	0.80000	0.75000
Precision (2-in-1)	0.87500	0.85714
Recall (2-in-1)	0.70000	0.60000
F1-score (2-in-1)	0.77778	0.70588

Table 8: Eggplant intersection-based clustering results on the validation set using radial intersection points (RIP) without density thresholding.

Panel of Figure 18 — Figure 18: Representative eggplant RIP-based clustering examples without density filtering using K-means on ground-truth 2-in-1 ROIs. The left panel shows a correctly resolved case, while the right panel shows a missed 2-in-1 case that is incorrectly classified as 1-in-1 because its silhouette score falls below the decision threshold. Cluster centroids are indicated by $\times$ .

Metric	K-means	GMM
Accuracy	0.70000	0.65000
Precision (2-in-1)	0.66667	0.66667
Recall (2-in-1)	0.80000	0.60000
F1-score (2-in-1)	0.72727	0.63158

Table 9: Eggplant intersection-based clustering results on the validation set using RIP with density thresholding.

Panel of Figure 20 — Figure 20: Representative eggplant RIP-based examples obtained with minimum-density filtering and K-means for ground-truth 2-in-1 ROIs. In each row, the left panel shows the raw intersection points and the right panel shows the final clustering result. The top row shows a correctly resolved case, while the bottom row shows a missed 2-in-1 case. Cluster centroids are indicated by $\times$ .

Panel of Figure 21 — Figure 21: Representative eggplant false-positive RIP result after minimum-density filtering using K-means. The left panel shows the raw intersection points, and the right panel shows the final filtered clustering output. Although the ROI is labeled as 1-in-1, the filtered RIP pipeline predicts 2-in-1. Cluster centroids are indicated by $\times$ .

Metric	K-means	GMM
Accuracy	0.55000	0.55000
Precision (2-in-1)	0.66667	1.00000
Recall (2-in-1)	0.20000	0.10000
F1-score (2-in-1)	0.30769	0.18182

Table 10: Tomato intersection-based clustering results on the validation set using radial intersection points (RIP) without density thresholding.

Metric	K-means	GMM
Accuracy	0.70000	0.35000
Precision (2-in-1)	0.70000	0.38462
Recall (2-in-1)	0.70000	0.50000
F1-score (2-in-1)	0.70000	0.43478

Table 11: Tomato intersection-based clustering results on the validation set using RIP with density thresholding.

Crop	Grid	K-means F1	GMM F1
	25 $\times$ 25	0.78	0.70
Eggplant	30 $\times$ 30	0.72	0.57
	35 $\times$ 35	0.70	0.625
	20 $\times$ 20	0.625	0.67
Tomato	25 $\times$ 25	0.78	0.57
	30 $\times$ 30	0.70	0.43

Table 12: Sensitivity of RIP-based clustering performance to grid resolution in the minimum-density filtering stage.

Metric	K-means	GMM
Accuracy	0.80000	0.80000
Precision (2-in-1)	0.80000	0.80000
Recall (2-in-1)	0.80000	0.80000
F1-score (2-in-1)	0.80000	0.80000

Table 13: Eggplant combined centroid-based and intersection-based clustering results on the validation set with density filtering.

Metric	K-means	GMM
Accuracy	0.80000	0.40000
Precision (2-in-1)	1.00000	0.40000
Recall (2-in-1)	0.60000	0.40000
F1-score (2-in-1)	0.75000	0.40000

Table 14: Tomato combined centroid-based and intersection-based clustering results on the validation set with density filtering.

UAV Remote Sensing

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CAMF-Det: Closure-Aware Multimodal Fusion for LiDAR-Camera 3D Object Detection on UAV Platforms

Yanze Jiang, Yanfeng Gu, Xian Li

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Lidar-Based 3D Object Detection Multimodal Robustness Camera-LiDAR Fusion 3D Object Detection Object Detection Multimodal Object Detection

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Multimodal 3D object detection based on LiDAR and cameras has demonstrated excellent performance in ground-vehicle scenarios, but has not been explored for Unmanned Aerial Vehicle (UAV) platforms. In UAV top-down scenes, frequent groundobject occlusion dominated by tree canopies causes spatially varying and modality-dependent information degradation. Existing multimodal fusion frameworks neither explicitly model such ground-object occlusion nor embed occlusion awareness into the detection pipeline, limiting their performance in occluded UAV scenes. To address these challenges, we propose CAMF-Det, a closure-aware multimodal fusion framework for LiDAR-camera 3D object detection on UAV platforms, which derives dual-modal occlusion intensity through physics-inspired modeling and embeds them as priors throughout the detection pipeline. First, a dual-modal closure modeling module explicitly constructs occlusion intensity ground truth for both modalities offline via a Beer-Lambert-inspired formulation and building-mask correction. Second, using these ground-truth maps as supervision, a dual-modal prediction network converts the offline modeling results into online occlusion intensity predictions under single-frame inference. Third, both ground-truth and predicted occlusion intensity are injected into data augmentation, feature encoding, multimodal fusion, and detection head, enabling adaptive detection under spatially varying and modality-dependent information degradation. Experiments on two self-built UAV-based multimodal datasets, SI3D-DI and SI3D-DII, demonstrate that CAMF-Det achieves the best performance across all difficulty levels, with hard-level mAP

_{\mathrm{BEV}}

improvements of 9.43% and 4.88% over the best competing methods, respectively. These results confirm the effectiveness of explicit occlusion prior modeling and exploitation for robust multimodal 3D detection in UAV scenes.

Abstract

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