Original scientific paper
https://doi.org/10.32985/ijeces.17.9.1
Enhanced YOLOv11 with Dual Attention Mechanisms for Fine-Grained Stomata Morphology Classification
Siti Andini Utiarahman
; Hasanuddin University, Faculty of Engineering, Department of Electrical Engineering Poros Malino Street, Gowa, Indonesia
Indrabayu -
; Hasanuddin University, Faculty of Engineering, Department of Electrical Engineering Poros Malino Street, Gowa, Indonesia
*
Ingrid Nurtanio
; Hasanuddin University, Faculty of Engineering, Department of Informatics Poros Malino Street, Gowa, Indonesia
* Corresponding author.
Abstract
Accurate classification of stomatal morphological types is essential for botanical taxonomy and pharmacognostic authentication of herbal plants; however, no prior system has simultaneously detected and classified all four types – Anomocytic, Diacytic, Paracytic, and Graminoid – within a unified multi-instance detection pipeline. This study proposes an Enhanced YOLOv11 architecture integrating the Convolutional Block Attention Module (CBAM) at two successive C2f backbone junctions for progressive feature refinement and Adaptive Spatial Feature Fusion (ASFF) at each of the three detection scales to resolve inter-scale feature conflicts. The model was trained on a dataset of 6259 microscopic images and 74424 annotated instances across four morphological types and eight herbal plant species. Ablation experiments confirm that the dual-attention configuration achieves an mAP50 of 0.895 and an F1-score of 0.846, with a nonlinear synergy gain of 7.53%, whereby the CBAM-ASFF model achieves a lower inference latency (114.2 ms) than the ASFF-only variant (121.7 ms) despite the near-identical computational cost (9.091 vs. 9.088 GFLOPs). When benchmarked against CA-YOLOv5, StomaYOLO, and YOLOv8s retrained on the same dataset, the proposed model achieved the highest mAP50 and F1-score while maintaining the lowest parameter count (3.371 M) among all evaluated architectures.
Keywords
YOLOv11; CBAM; ASFF; Stomatal Classification; fine-grained object detection;
Hrčak ID:
352135
URI
Publication date:
14.10.2026.
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