Info Valley Korea Co., Ltd., Anyang-si 14067, Korea.
Department of Computer Convergence Software, Korea University, Sejong 30019, Korea.
Sensors (Basel). 2022 Mar 31;22(7):2689. doi: 10.3390/s22072689.
Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this "light-weight" method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.
了解大型养猪场的猪的数量对于高效的农场管理是很重要的。然而，因为猪不会乖乖地停下来或减速以供计数，所以准确地数猪对人类来说是很困难的。在这项研究中，我们提出了一种基于摄像头的自动方法来计算通过计数区域的猪的数量。也就是说，我们的基于深度学习的视频目标检测和跟踪方法使用走廊中的摄像头分析视频流并计算通过计数区域的猪的数量。此外，为了在低成本的嵌入式板上实时执行计数方法，我们考虑了准确性和执行时间之间的权衡，这对于猪的计数来说还没有被报道过。我们在 NVIDIA Jetson Nano 嵌入式板上的实验结果表明，这种“轻量级”的方法对于计算通过的猪的数量是有效的，无论是在准确性（即 99.44%）还是在执行时间（即实时执行）方面，即使有些猪在计数区域内来回穿过。
