Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations

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.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information

Son

Ahn

Baek

et al. 2022

“…Early methods used statistical classification [ 30 ], DTW distance [ 31 ], and discriminant analysis [ 32 ] to classify selected dog behaviors, providing proof of concept. Later, with the improvement in machine learning techniques and the increase in their popularity in different fields of research [ 36 , 38 , 39 , 40 ], more methods have been employed to recognize dog behaviors. For instance, Chambers et al [ 34 ] aimed to validate dog behavior recognition, with a focus on eating and drinking, using FilterNet and a large crowd-sourced dataset.…”

Section: Introductionmentioning

confidence: 99%

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

Atif

Lee

Park

et al. 2023

Self Cite

The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs’ health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog’s behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog’s behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog’s location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog’s health and welfare.

“…Recently, many studies have been conducted on automatic livestock monitoring using various methods. Some of these include wearable sensor-based [ 6 , 7 , 8 ], audio-based [ 9 , 10 , 11 , 12 , 13 ], or video-based methods [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Compared with sensor-based methods, video-based methods can provide a non-invasive, non-stressful, and intuitive way to monitor the behavior of an individual or a group of pigs where the sensor-based method fail to do.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with audio-based methods, video-based methods can intuitively grasp a pig’s health condition. In contrast, specialists are always required for further sound analysis and recognition in audio-based methods [ 9 , 10 , 11 ]. Thus, video-based methods have received the most attention in recent studies.…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System

Zhao

Atif

Lee

et al. 2022

Self Cite

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.