BiAF: research on dynamic goat herd detection and tracking based on machine vision

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As technology advances, rangeland management is rapidly transitioning toward intelligent systems. To optimize grassland resources and implement scientific grazing practices, livestock grazing monitoring has become a pivotal area of research. Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. The BiAF-YOLOv7 algorithm achieves precision, recall, F1 score, and mAP values of 94.5, 96.7, 94.8, and 96.0%, respectively, on the goat herd dataset. Combined with DeepSORT, our system successfully tracks goat herds, demonstrating the effectiveness of the BiAF-YOLOv7 algorithm as a tool for livestock grazing monitoring. This study not only validates the practicality of the proposed algorithm but also highlights the broader applicability of machine vision-based monitoring in large-scale environments. It provides innovative approaches to achieve grass-animal balance through information-driven methods, such as monitoring and tracking.