Department of Computer Convergence Software, Korea University, Sejong 30019, Korea.
Info Valley Korea Co., Ltd., Anyang 14067, Korea.
Sensors (Basel). 2022 Oct 29;22(21):8315. doi: 10.3390/s22218315.
The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.
个体猪的自动检测可以提高养猪场的整体管理水平。近年来，随着深度学习技术的进步，单图像目标检测的准确性有了显著提高。然而，商业养猪场猪圈中猪的大小差异和复杂结构，如饲养设施，对猪监测的检测精度提出了挑战。为了在实践中实现这种检测，应该通过静态摄像机记录的视频来分析这些差异。为了准确检测可能大小不同或被复杂结构遮挡的个体猪，我们提出了一种基于深度学习的目标检测方法，该方法利用从静态摄像机记录的图像序列（即视频）中生成的背景和设施信息，这些信息包含相关信息。由于所有图像都经过预处理以减少猪大小的差异。然后，我们使用提取的背景和设施信息来创建不同的灰度图像组合。最后，将这些图像组合成不同的三通道合成图像组合，用作训练数据集以提高检测准确性。将所提出的方法作为图像处理的一个组成部分，可以将整体准确性从 84%提高到 94%。从研究中可以看出，经过长时间的更新，能够生成准确的设施和背景图像，有助于提高检测准确性。对于进一步的研究，还可以考虑提高重叠猪的检测准确性。
