Lee Jeyoung, Kang Hochul
Department of Digital Media, The Catholic University of Korea, Bucheon 14662, Korea.
J Anim Sci Technol. 2024 Jul;66(4):846-858. doi: 10.5187/jast.2023.e76. Epub 2024 Jul 31.
In a duck cage, ducks are placed in various states. In particular, if a duck is overturned and falls or dies, it will adversely affect the growing environment. In order to prevent the foregoing, it was necessary to continuously manage the cage for duck growth. This study proposes a method using an object detection algorithm to improve the foregoing. Object detection refers to the work to perform classification and localization of all objects present in the image when an input image is given. To use an object detection algorithm in a duck cage, data to be used for learning should be made and the data should be augmented to secure enough data to learn from. In addition, the time required for object detection and the accuracy of object detection are important. The study collected, processed, and augmented image data for a total of two years in 2021 and 2022 from the duck cage. Based on the objects that must be detected, the data collected as such were divided at a ratio of 9 : 1, and learning and verification were performed. The final results were visually confirmed using images different from the images used for learning. The proposed method is expected to be used for minimizing human resources in the growing process in duck cages and making the duck cages into smart farms.
在鸭笼中，鸭子处于各种状态。特别是，如果一只鸭子被打翻、摔倒或死亡，这将对生长环境产生不利影响。为了防止上述情况发生，有必要持续管理鸭笼以促进鸭子生长。本研究提出一种使用目标检测算法来改善上述情况的方法。目标检测是指在给定输入图像时，对图像中存在的所有对象进行分类和定位的工作。要在鸭笼中使用目标检测算法，需要制作用于学习的数据，并且应该对数据进行增强以确保有足够的数据用于学习。此外，目标检测所需的时间和目标检测的准确性也很重要。该研究在2021年和2022年共两年时间里从鸭笼收集、处理和增强图像数据。根据必须检测的对象，将这样收集到的数据按9:1的比例进行划分，并进行学习和验证。最终结果使用与学习所用图像不同的图像进行直观确认。预计所提出的方法将用于在鸭笼养殖过程中尽量减少人力资源，并将鸭笼转变为智能农场。
