Wang Shunli, Jiang Honghua, Qiao Yongliang, Jiang Shuzhen
College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.
Australian Institute for Machine Learning (AIML), The University of Adelaide, Adelaide, SA 5005, Australia.
Animals (Basel). 2023 Jul 31;13(15):2472. doi: 10.3390/ani13152472.
This paper proposes a method for automatic pig detection and segmentation using RGB-D data for precision livestock farming. The proposed method combines the enhanced YOLOv5s model with the Res2Net bottleneck structure, resulting in improved fine-grained feature extraction and ultimately enhancing the precision of pig detection and segmentation in 2D images. Additionally, the method facilitates the acquisition of 3D point cloud data of pigs in a simpler and more efficient way by using the pig mask obtained in 2D detection and segmentation and combining it with depth information. To evaluate the effectiveness of the proposed method, two datasets were constructed. The first dataset consists of 5400 images captured in various pig pens under diverse lighting conditions, while the second dataset was obtained from the UK. The experimental results demonstrated that the improved YOLOv5s_Res2Net achieved a mAP@0.5:0.95 of 89.6% and 84.8% for both pig detection and segmentation tasks on our dataset, while achieving a mAP@0.5:0.95 of 93.4% and 89.4% on the Edinburgh pig behaviour dataset. This approach provides valuable insights for improving pig management, conducting welfare assessments, and estimating weight accurately.
本文提出了一种利用RGB-D数据进行生猪自动检测与分割的方法，用于精准畜牧养殖。所提出的方法将增强型YOLOv5s模型与Res2Net瓶颈结构相结合，从而改进了细粒度特征提取，并最终提高了二维图像中生猪检测与分割的精度。此外，该方法通过使用二维检测与分割中获得的猪的掩码，并将其与深度信息相结合，以更简单、高效的方式获取猪的三维点云数据。为了评估所提出方法的有效性，构建了两个数据集。第一个数据集由在不同光照条件下的各种猪舍中拍摄的5400张图像组成，而第二个数据集来自英国。实验结果表明，改进后的YOLOv5s_Res2Net在我们的数据集上进行生猪检测和分割任务时，平均精度均值（mAP）@0.5:0.95分别达到89.6%和84.8%，而在爱丁堡生猪行为数据集上，mAP@0.5:0.95分别达到93.4%和89.4%。这种方法为改善生猪管理、进行福利评估和准确估计体重提供了有价值的见解。
