Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments
Accurate pig counting is crucial for precision livestock farming, enabling optimized feeding management and health monitoring. Detection-based counting methods face significant challenges due to mutual occlusion, varying illumination conditions, diverse pen configurations, and substantial variations...
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MDPI AG
2025-07-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/14/2149 |
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| author | Jian Li Wenkai Ma Yanan Wei Tan Wang |
| author_facet | Jian Li Wenkai Ma Yanan Wei Tan Wang |
| author_sort | Jian Li |
| collection | DOAJ |
| description | Accurate pig counting is crucial for precision livestock farming, enabling optimized feeding management and health monitoring. Detection-based counting methods face significant challenges due to mutual occlusion, varying illumination conditions, diverse pen configurations, and substantial variations in pig densities. Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. To address these limitations, we propose EAPC-YOLO (enhanced adaptive pig counting YOLO), a robust architecture integrating density-aware processing with advanced detection optimizations. The method consists of (1) an enhanced YOLOv8 network incorporating multiple architectural improvements for better feature extraction and object localization. These improvements include DCNv4 deformable convolutions for irregular pig postures, BiFPN bidirectional feature fusion for multi-scale information integration, EfficientViT linear attention for computational efficiency, and PIoU v2 loss for improved overlap handling. (2) A density-aware post-processing module with intelligent NMS strategies that adapt to different crowding scenarios. Experimental results on a comprehensive dataset spanning diverse agricultural scenarios (nighttime, controlled indoor, and natural daylight environments with density variations from 4 to 30 pigs) demonstrate our method achieves 94.2% mAP@0.5 for detection performance and 96.8% counting accuracy, representing 12.3% and 15.7% improvements compared to the strongest baseline, YOLOv11n. This work enables robust, accurate pig counting across challenging agricultural environments, supporting precision livestock management. |
| format | Article |
| id | doaj-art-31652897652e4af987fe10ea0e5a94d0 |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-31652897652e4af987fe10ea0e5a94d02025-08-20T03:55:48ZengMDPI AGAnimals2076-26152025-07-011514214910.3390/ani15142149Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural EnvironmentsJian Li0Wenkai Ma1Yanan Wei2Tan Wang3School of Biology and Food Engineering, Fuyang Normal University, Fuyang 236037, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, ChinaAnhui Provincial Key Laboratory of Embryo Development and Reproductive Regulation, Fuyang 236037, ChinaSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, ChinaAccurate pig counting is crucial for precision livestock farming, enabling optimized feeding management and health monitoring. Detection-based counting methods face significant challenges due to mutual occlusion, varying illumination conditions, diverse pen configurations, and substantial variations in pig densities. Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. To address these limitations, we propose EAPC-YOLO (enhanced adaptive pig counting YOLO), a robust architecture integrating density-aware processing with advanced detection optimizations. The method consists of (1) an enhanced YOLOv8 network incorporating multiple architectural improvements for better feature extraction and object localization. These improvements include DCNv4 deformable convolutions for irregular pig postures, BiFPN bidirectional feature fusion for multi-scale information integration, EfficientViT linear attention for computational efficiency, and PIoU v2 loss for improved overlap handling. (2) A density-aware post-processing module with intelligent NMS strategies that adapt to different crowding scenarios. Experimental results on a comprehensive dataset spanning diverse agricultural scenarios (nighttime, controlled indoor, and natural daylight environments with density variations from 4 to 30 pigs) demonstrate our method achieves 94.2% mAP@0.5 for detection performance and 96.8% counting accuracy, representing 12.3% and 15.7% improvements compared to the strongest baseline, YOLOv11n. This work enables robust, accurate pig counting across challenging agricultural environments, supporting precision livestock management.https://www.mdpi.com/2076-2615/15/14/2149pig detectiondensity-aware countingYOLOv8precision livestock farmingcomputer vision |
| spellingShingle | Jian Li Wenkai Ma Yanan Wei Tan Wang Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments Animals pig detection density-aware counting YOLOv8 precision livestock farming computer vision |
| title | Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments |
| title_full | Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments |
| title_fullStr | Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments |
| title_full_unstemmed | Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments |
| title_short | Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments |
| title_sort | enhanced yolov8 for robust pig detection and counting in complex agricultural environments |
| topic | pig detection density-aware counting YOLOv8 precision livestock farming computer vision |
| url | https://www.mdpi.com/2076-2615/15/14/2149 |
| work_keys_str_mv | AT jianli enhancedyolov8forrobustpigdetectionandcountingincomplexagriculturalenvironments AT wenkaima enhancedyolov8forrobustpigdetectionandcountingincomplexagriculturalenvironments AT yananwei enhancedyolov8forrobustpigdetectionandcountingincomplexagriculturalenvironments AT tanwang enhancedyolov8forrobustpigdetectionandcountingincomplexagriculturalenvironments |