PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets
Aggressive behavior among piglets is considered a harmful social contact. Monitoring weaned piglets with intense aggressive behaviors is paramount for pig breeding management. This study introduced a novel hybrid model, PAB-Mamba-YOLO, integrating the principles of Mamba and YOLO for efficient visua...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000017 |
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| author | Xue Xia Ning Zhang Zhibin Guan Xin Chai Shixin Ma Xiujuan Chai Tan Sun |
| author_facet | Xue Xia Ning Zhang Zhibin Guan Xin Chai Shixin Ma Xiujuan Chai Tan Sun |
| author_sort | Xue Xia |
| collection | DOAJ |
| description | Aggressive behavior among piglets is considered a harmful social contact. Monitoring weaned piglets with intense aggressive behaviors is paramount for pig breeding management. This study introduced a novel hybrid model, PAB-Mamba-YOLO, integrating the principles of Mamba and YOLO for efficient visual detection of weaned piglets' aggressive behaviors, including climbing body, nose hitting, biting tail and biting ear. Within the proposed model, a novel CSPVSS module, which integrated the Cross Stage Partial (CSP) structure with the Visual State Space Model (VSSM), has been developed. This module was adeptly integrated into the Neck part of the network, where it harnessed convolutional capabilities for local feature extraction and leveraged the visual state space to reveal long-distance dependencies. The model exhibited sound performance in detecting aggressive behaviors, with an average precision (AP) of 0.976 for climbing body, 0.994 for nose hitting, 0.977 for biting tail and 0.994 for biting ear. The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. Comparative experiments with existing prevailing models confirmed the superiority of the proposed model. This work is expected to contribute a glimmer of fresh ideas and inspiration to the research field of precision breeding and behavioral analysis of animals. |
| format | Article |
| id | doaj-art-8ba87e5e1f1149289b1e87e04ef8bd2a |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-8ba87e5e1f1149289b1e87e04ef8bd2a2025-01-13T04:19:06ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-03-011515266PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned pigletsXue Xia0Ning Zhang1Zhibin Guan2Xin Chai3Shixin Ma4Xiujuan Chai5Tan Sun6Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Corresponding authors.Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China; Corresponding authors.Aggressive behavior among piglets is considered a harmful social contact. Monitoring weaned piglets with intense aggressive behaviors is paramount for pig breeding management. This study introduced a novel hybrid model, PAB-Mamba-YOLO, integrating the principles of Mamba and YOLO for efficient visual detection of weaned piglets' aggressive behaviors, including climbing body, nose hitting, biting tail and biting ear. Within the proposed model, a novel CSPVSS module, which integrated the Cross Stage Partial (CSP) structure with the Visual State Space Model (VSSM), has been developed. This module was adeptly integrated into the Neck part of the network, where it harnessed convolutional capabilities for local feature extraction and leveraged the visual state space to reveal long-distance dependencies. The model exhibited sound performance in detecting aggressive behaviors, with an average precision (AP) of 0.976 for climbing body, 0.994 for nose hitting, 0.977 for biting tail and 0.994 for biting ear. The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. Comparative experiments with existing prevailing models confirmed the superiority of the proposed model. This work is expected to contribute a glimmer of fresh ideas and inspiration to the research field of precision breeding and behavioral analysis of animals.http://www.sciencedirect.com/science/article/pii/S2589721725000017Aggressive behaviorsWeaned pigletMambaYOLOHybrid detection model |
| spellingShingle | Xue Xia Ning Zhang Zhibin Guan Xin Chai Shixin Ma Xiujuan Chai Tan Sun PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets Artificial Intelligence in Agriculture Aggressive behaviors Weaned piglet Mamba YOLO Hybrid detection model |
| title | PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets |
| title_full | PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets |
| title_fullStr | PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets |
| title_full_unstemmed | PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets |
| title_short | PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets |
| title_sort | pab mamba yolo vssm assists in yolo for aggressive behavior detection among weaned piglets |
| topic | Aggressive behaviors Weaned piglet Mamba YOLO Hybrid detection model |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000017 |
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