Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs
Abstract Timely and accurate detection of ear tag dropout is crucial for standardized precision breeding, health monitoring, and breeding evaluation. Reserve breeding pigs exhibit high activity levels and frequent interactions, leading to a higher prevalence of ear tag dropout. However, detection is...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09679-4 |
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| author | Weijun Duan Fang Wang Xueliang Fu Honghui Li Buyu Wang |
| author_facet | Weijun Duan Fang Wang Xueliang Fu Honghui Li Buyu Wang |
| author_sort | Weijun Duan |
| collection | DOAJ |
| description | Abstract Timely and accurate detection of ear tag dropout is crucial for standardized precision breeding, health monitoring, and breeding evaluation. Reserve breeding pigs exhibit high activity levels and frequent interactions, leading to a higher prevalence of ear tag dropout. However, detection is challenging due to motion blur, small tag size, and significant target scale variations. To address this, we propose a motion blur-aware multi-scale framework, Adapt-Cascade. First, a Weight-Adaptive Attention Module (WAAM) enhances the extraction of motion blur features. Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. Third, a Feature-Guided Multi-Scale Region Proposal strategy (FGMS-RP) strengthens multi-scale target detection. Integrated into the Cascade Mask R-CNN framework with Focal Loss, Adapt-Cascade achieves 93.46% accuracy at 19.2 frames per second in detecting ear tag dropout in reserve breeding pigs. This model provides a high-accuracy solution for intelligent pig farm management. |
| format | Article |
| id | doaj-art-a95afb98cd1e48908d8f171eb71948c0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a95afb98cd1e48908d8f171eb71948c02025-08-20T04:01:51ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-09679-4Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigsWeijun Duan0Fang Wang1Xueliang Fu2Honghui Li3Buyu Wang4College of Computer and Information Engineering, Inner Mongolia Agricultural UniversityCollege of Computer and Information Engineering, Inner Mongolia Agricultural UniversityCollege of Computer and Information Engineering, Inner Mongolia Agricultural UniversityCollege of Computer and Information Engineering, Inner Mongolia Agricultural UniversityCollege of Computer and Information Engineering, Inner Mongolia Agricultural UniversityAbstract Timely and accurate detection of ear tag dropout is crucial for standardized precision breeding, health monitoring, and breeding evaluation. Reserve breeding pigs exhibit high activity levels and frequent interactions, leading to a higher prevalence of ear tag dropout. However, detection is challenging due to motion blur, small tag size, and significant target scale variations. To address this, we propose a motion blur-aware multi-scale framework, Adapt-Cascade. First, a Weight-Adaptive Attention Module (WAAM) enhances the extraction of motion blur features. Second, Density-Aware Dilated Convolution (DA-DC) dynamically adjusts the convolutional receptive field to improve small ear tag detection. Third, a Feature-Guided Multi-Scale Region Proposal strategy (FGMS-RP) strengthens multi-scale target detection. Integrated into the Cascade Mask R-CNN framework with Focal Loss, Adapt-Cascade achieves 93.46% accuracy at 19.2 frames per second in detecting ear tag dropout in reserve breeding pigs. This model provides a high-accuracy solution for intelligent pig farm management.https://doi.org/10.1038/s41598-025-09679-4Adapt-CascadeEar tag dropoutMotion blur adaptationMulti-scale detectionReserve breeding pigs |
| spellingShingle | Weijun Duan Fang Wang Xueliang Fu Honghui Li Buyu Wang Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs Scientific Reports Adapt-Cascade Ear tag dropout Motion blur adaptation Multi-scale detection Reserve breeding pigs |
| title | Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| title_full | Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| title_fullStr | Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| title_full_unstemmed | Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| title_short | Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| title_sort | motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs |
| topic | Adapt-Cascade Ear tag dropout Motion blur adaptation Multi-scale detection Reserve breeding pigs |
| url | https://doi.org/10.1038/s41598-025-09679-4 |
| work_keys_str_mv | AT weijunduan motionblurawaremultiscaleadaptivecascadeframeworkforeartagdropoutdetectioninreservebreedingpigs AT fangwang motionblurawaremultiscaleadaptivecascadeframeworkforeartagdropoutdetectioninreservebreedingpigs AT xueliangfu motionblurawaremultiscaleadaptivecascadeframeworkforeartagdropoutdetectioninreservebreedingpigs AT honghuili motionblurawaremultiscaleadaptivecascadeframeworkforeartagdropoutdetectioninreservebreedingpigs AT buyuwang motionblurawaremultiscaleadaptivecascadeframeworkforeartagdropoutdetectioninreservebreedingpigs |