Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Bhujel, Anil; Arulmozhi, Elanchezhıan; Moon, Byeong-Eun; Kim, Hyeon Tae

doi:10.3390/ani11113089

Cited by 10 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of behavior tracking, Alameer et al (2020) adopted the Faster R-CNN and YOLOv2 as the detectors and DeepSORT as the tracker to overcome illumination changes and the occlusions of pigs in the commercial environment. In addition, a deep-learningbased pig posture and locomotion activity detection and DeepSORT tracking algorithms were designed to measure pig behavior changes in an experimental pig barn at different greenhouse gas (GHG) levels [18]. These approaches were good at detection and tracking behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…A probabilistic tracking-by-detection method was proposed, which first used a fully convolutional detector to detect visible key points of individual pigs and then tracked individual animals in a group setting [17]. A deep-learning-based pig posture and tracking algorithm were designed to measure those behavior changes in an experimental pig barn at different greenhouse gas (GHG) levels [18]. However, pig behavior detection and tracking still face some difficulties, such as target occlusion, varying light, overlapping, and error IDs of tracks in tracking [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method

Zeng

Liang

et al. 2022

Agriculture

View full text Add to dashboard Cite

Pig behavior recognition and tracking in group-housed livestock are effective aids for health and welfare monitoring in commercial settings. However, due to demanding farm conditions, the targets in the pig videos are heavily occluded and overlapped, and there are illumination changes, which cause error switches of pig identify (ID) in the tracking process and decrease the tracking quality. To solve these problems, this study proposed an improved DeepSORT algorithm for object tracking, which contained three processes. Firstly, two detectors, YOLOX-S and YOLO v5s, were developed to detect pig targets and classify four types of pig behaviors including lying, eating, standing, and other. Then, the improved DeepSORT was developed for pig behavior tracking and reducing error changes of pig ID by improving trajectory processing and data association. Finally, we established the public dataset annotation of group-housed pigs, with 3600 images in a total from 12 videos, which were suitable for pig tracking applications. The advantage of our method includes two aspects. One is that the trajectory processing and data association are improved by aiming at pig-specific scenarios, which are indoor scenes, and the number of pig target objects is stable. This improvement reduces the error switches of pig ID and enhances the stability of the tracking. The other is that the behavior classification information from the detectors is introduced into the tracking algorithm for behavior tracking. In the experiments of pig detection and behavior recognition, the YOLO v5s and YOLOX-S detectors achieved a high precision rate of 99.4% and 98.43%, a recall rate of 99% and 99.23, and a mean average precision (mAP) rate of 99.50% and 99.23%, respectively, with an AP.5:.95 of 89.3% and 87%. In the experiments of pig behavior tracking, the improved DeepSORT algorithm based on YOLOX-S obtained multi-object tracking accuracy (MOTA), ID switches (IDs), and IDF1 of 98.6%,15, and 95.7%, respectively. Compared with DeepSORT, it improved by 1.8% and 6.8% in MOTA and IDF1, respectively, and IDs had a significant decrease, with a decline of 80%. These experiments demonstrate that the improved DeepSORT can achieve pig behavior tracking with stable ID values under commercial conditions and provide scalable technical support for contactless automated pig monitoring.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method

Zeng

Liang

et al. 2022

Agriculture

View full text Add to dashboard Cite

show abstract

“…Integrating gas sensors with ventilation systems helps control the indoor climate and maintain a healthy environment for animals and workers. High carbon dioxide CO 2 levels can pose a suffocation risk [ 13 , 14 ]. Using mechanisms that capture spatial–temporal correlations and dependencies among diverse data streams has proven highly effective.…”

Section: Introductionmentioning

confidence: 99%

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

Kakar,

Hussain,

Kim

et al. 2024

Sensors

View full text Add to dashboard Cite

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called “TimeTector-Twin-Branch Shared LSTM Autoencoder” which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

show abstract

“…Wang M et al [23] optimized FairMOT with a deep learning algorithm, realizing the recognition and tracking of reappearing pig individuals and improving the accuracy of multi-target recognition and tracking. Bhujel A et al [24] proposed a deep-learning-based pig individual and motion state detection and tracking algorithm and found that the YOLOv4 detector combined with the deep sorting tracking algorithm improves performance in pig multi-object detection and tracking. Zhang L et al [25] proposed a method for individual detection and tracking of live pigs that achieves a recognition accuracy of 94.72% and a recall rate of 94.74%, providing appearance features for the tracker.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network

Shi

Jiao

2023

Sensors

View full text Add to dashboard Cite

Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets—pig faces and pig necks—which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model’s target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices.

show abstract

Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Cited by 10 publications

References 47 publications

Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method

Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network

Contact Info

Product

Resources

About