A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone
Soldier crabs (Mictyris spp.) are widely recognized as ecological indicators in intertidal ecosystems. Their behavioral rhythms respond sensitively to environmental fluctuations, especially variations in sediment composition and illumination. However, due to their small size and tidal-driven activit...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003474 |
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| author | Liangjun Li Zhihao Ren Cheng Tang Shengning Lu Yong Liang |
| author_facet | Liangjun Li Zhihao Ren Cheng Tang Shengning Lu Yong Liang |
| author_sort | Liangjun Li |
| collection | DOAJ |
| description | Soldier crabs (Mictyris spp.) are widely recognized as ecological indicators in intertidal ecosystems. Their behavioral rhythms respond sensitively to environmental fluctuations, especially variations in sediment composition and illumination. However, due to their small size and tidal-driven activity patterns, conventional behavior detection methods suffer from low efficiency and considerable observer bias, particularly under dark conditions where detection errors and omissions are prevalent. To address these limitations, we propose a lightweight deep learning model, SFS-YOLO, built upon the YOLOv11n framework, for accurate behavior detection of soldier crabs under varying illumination. The model integrates an SPD-Conv module for efficient downsampling, a fine-grained channel attention mechanism for enhanced feature representation, and a robust SIoU loss function to improve the localization of small targets. Experimental results demonstrate that SFS-YOLO improves mean average precision from 92.4% to 94.8%, increases recall from 93.0% to 93.6%, reduces the number of parameters by 8.33%, and compresses the model size by 0.4 MB. The model achieves mAP scores of 95.4% and 93.2% under bright and dark conditions, respectively. Beyond detection performance, this study investigates sediment-specific behavioral rhythms and identifies light-dependent temporal patterns that may serve as early ecological indicators of habitat perturbation. The proposed approach offers a practical solution for intelligent monitoring of small benthic organisms in intertidal zones. Moreover, the rhythm-based indicators developed herein show strong potential for applications in biodiversity assessment and sustainable ecosystem management within dynamic coastal environments. |
| format | Article |
| id | doaj-art-d97feb8bd997447983d386ad54095ecc |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-d97feb8bd997447983d386ad54095ecc2025-08-20T05:05:51ZengElsevierEcological Informatics1574-95412025-12-019010333810.1016/j.ecoinf.2025.103338A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zoneLiangjun Li0Zhihao Ren1Cheng Tang2Shengning Lu3Yong Liang4Key Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China; College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China; College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China; College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China; College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, ChinaCorresponding author at: College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China.; Key Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China; College of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, ChinaSoldier crabs (Mictyris spp.) are widely recognized as ecological indicators in intertidal ecosystems. Their behavioral rhythms respond sensitively to environmental fluctuations, especially variations in sediment composition and illumination. However, due to their small size and tidal-driven activity patterns, conventional behavior detection methods suffer from low efficiency and considerable observer bias, particularly under dark conditions where detection errors and omissions are prevalent. To address these limitations, we propose a lightweight deep learning model, SFS-YOLO, built upon the YOLOv11n framework, for accurate behavior detection of soldier crabs under varying illumination. The model integrates an SPD-Conv module for efficient downsampling, a fine-grained channel attention mechanism for enhanced feature representation, and a robust SIoU loss function to improve the localization of small targets. Experimental results demonstrate that SFS-YOLO improves mean average precision from 92.4% to 94.8%, increases recall from 93.0% to 93.6%, reduces the number of parameters by 8.33%, and compresses the model size by 0.4 MB. The model achieves mAP scores of 95.4% and 93.2% under bright and dark conditions, respectively. Beyond detection performance, this study investigates sediment-specific behavioral rhythms and identifies light-dependent temporal patterns that may serve as early ecological indicators of habitat perturbation. The proposed approach offers a practical solution for intelligent monitoring of small benthic organisms in intertidal zones. Moreover, the rhythm-based indicators developed herein show strong potential for applications in biodiversity assessment and sustainable ecosystem management within dynamic coastal environments.http://www.sciencedirect.com/science/article/pii/S1574954125003474Behavior detectionSoldier crabDeep learningAttention mechanismBehavior rhythmEcological indicator |
| spellingShingle | Liangjun Li Zhihao Ren Cheng Tang Shengning Lu Yong Liang A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone Ecological Informatics Behavior detection Soldier crab Deep learning Attention mechanism Behavior rhythm Ecological indicator |
| title | A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone |
| title_full | A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone |
| title_fullStr | A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone |
| title_full_unstemmed | A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone |
| title_short | A deep learning-based detection model and illumination-adaptive behavioral analysis for soldier crabs in the intertidal zone |
| title_sort | deep learning based detection model and illumination adaptive behavioral analysis for soldier crabs in the intertidal zone |
| topic | Behavior detection Soldier crab Deep learning Attention mechanism Behavior rhythm Ecological indicator |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003474 |
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