PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets

Abstract

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.