Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea.
Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea.
Sensors (Basel). 2022 May 22;22(10):3917. doi: 10.3390/s22103917.
Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.
红外摄像机允许进行非侵入式和 24 小时连续监测。因此，它们被广泛应用于自动猪只监测中，这对于保持集约化养猪场的盈利能力和可持续性至关重要。然而，在实际应用中，昆虫分泌物等杂质会不断污染摄像机镜头。这会导致红外反射问题，严重影响猪只检测性能。在本研究中，我们提出了一种抗噪的实时猪只检测系统，可以提高在遭受红外反射问题困扰的养殖场中的检测精度。该系统由数据采集器、预处理和检测器组成，用于采集红外图像、将噪声图像转换为清洁图像以及检测猪只。预处理将多尺度空间注意力模块嵌入 U-Net 和生成对抗网络 (GAN) 模型中，使模型能够更加关注噪声区域。GAN 模型使用配对的清洁数据和模拟噪声数据进行训练，能够实现实时和端到端的操作。实验结果表明，与传统方法相比，所提出的预处理方法能够显著提高猪只检测的平均精度，从 0.766 提高到 0.906，在 PC 环境下仅增加了 4.8 毫秒的额外执行时间。
