Motion blur aware multiscale adaptive cascade framework for ear tag dropout detection in reserve breeding pigs

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.