Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning

Riekert, Martin; Opderbeck, Svenja; Wild, Andrea; Gallmann, Eva

doi:10.1016/j.compag.2021.106213

Cited by 15 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method proposed in this paper achieved a result of 2.5% higher if the sitting posture was included, and 5.3% higher if the sitting posture was not included, which proves the effectiveness of the method. Other researchers performed posture recognition from different camera views with 2D cameras [28][29][30], but the detection of sitting postures in pigs was not included in their experiments. It can be inferred that the recognition effect for sitting postures in the unmodified model shown in Table 5 was far lower than that for standing and lying postures.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, if models from the previous study directly recognize the sitting posture of pigs, the detection effect may decline. In the research of Riekert et al [28,29], Faster-RCNN was used to detect pig postures. As shown in Table 6, the speed of Faster-RCNN was lower than that of the method used in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the lower activity of pigs at night, the postures of pigs remained unchanged for a long time, so the recording time at night was from 8:00 p.m. to 12:00 p.m. The videos collected at different time periods and in different pens were processed by frame extraction, and the video frame images were extracted every 20 s. With reference to the literature [24][25][26][27][28][29][30], we selected three types of pig postures for recognition, namely, standing, lying, and sitting postures. The pig posture descriptions are shown in Table 1.…”

Section: Datasetmentioning

confidence: 99%

“…Nasirahmadi et al [27] combined R-FCN and ResNet101 to recognize pig postures such as standing, lying on the side, and lying on the stomach. Riekert et al [28,29] improved Faster R-CNN to detect the positions of pigs and recognize lying and non-lying postures. Shao et al [30] extracted individual pigs from pictures of herds through YOLOv5 and then used the DeepLab v3+ segmentation method to extract individual pig contours, followed by recognizing postures in a deep separable convolutional network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning

Lao

et al. 2022

Agriculture

View full text Add to dashboard Cite

The accurate and rapid detection of objects in videos facilitates the identification of abnormal behaviors in pigs and the introduction of preventive measures to reduce morbidity. In addition, accurate and effective pig detection algorithms provide a basis for pig behavior analysis and management decision-making. Monitoring the posture of pigs can enable the detection of the precursors of pig diseases in a timely manner and identify factors that impact pigs’ health, which helps to evaluate their health status and comfort. Excessive sitting represents abnormal behavior when pigs are frustrated in a restricted environment. The present study focuses on the automatic recognition of standing posture and lying posture in grouped pigs, which shows a lack of recognition of sitting posture. The main contributions of this paper are as follows: A human-annotated dataset of standing, lying, and sitting postures captured by 2D cameras during the day and night in a pig barn was established, and a simplified copy, paste, and label smoothing strategy was applied to solve the problem of class imbalance caused by the lack of sitting postures among pigs in the dataset. The improved YOLOX has an average precision with an intersection over union threshold of 0.5 (AP0.5) of 99.5% and average precision with an intersection over union threshold of 0.5–0.95 (AP0.5–0.95) of 91% in pig position detection; an AP0.5 of 90.9% and an AP0.5–0.95 of 82.8% in sitting posture recognition; a mean average precision with intersection over union threshold of 0.5 (mAP0.5) of 95.7% and a mean average precision with intersection over union threshold of 0.5–0.95 (mAP0.5–0.95) of 87.2% in all posture recognition. The method proposed in our study can improve the position detection and posture recognition of grouped pigs effectively, especially for pig sitting posture recognition, and can meet the needs of practical application in pig farms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning

Lao

et al. 2022

Agriculture

View full text Add to dashboard Cite

show abstract

“…e results demonstrated that in real-world data, this method got a mean absolute error of 1.67. Riekert et al [17] used a deep learning system to detect the position and pose of pigs and achieved 84% mAP@.50 for the day and 58% mAP@.50 for the night. Although scholars have studied pig identification, the accuracy and speed of detection for pig identification are not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

Zishun

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Information-based pig detection and counting is the trend in smart animal husbandry development. Cameras can efficiently collect farm information and combine it with artificial intelligence technology to assist breeders in real-time monitoring and analysis of farming. In order to improve the speed and accuracy of pig detection and counting, an advanced improved YOLO_v5 method for pig detection and counting based on the attention mechanism is proposed. The model is named as YOLOV5_Plus. This article utilizes a series of data augmentation methods, including translation, color augmentation, rescaling, and mosaic. The proposed model performs feature extraction on the original image with a backbone network, detects pigs of different sizes with three detection heads, and counts the detected anchor frames. Different versions of YOLOV5 are compared, and YOLOV5x is selected as the baseline model for the best performance. Attention modules are smartly combined with the model so that the model can better handle overlapping and misidentification. YOLOV5_Plus can achieve an accuracy of 0.989, a recall of 0.996, mAP@.50 of 0.994, and mAP@.50:.95 of 0.796, which outperforms all competing models. The inference time per image during detection is only 24.1 ms. YOLOV5_Plus model achieves real-time pig number and location detection, which is meaningful for promoting smart animal husbandry and saving labor costs in farming enterprises.

show abstract

Automated detection and quantification of contact behaviour in pigs using deep learning

Alameer

Buijs

O’Connell

et al. 2022

Biosystems Engineering

View full text Add to dashboard Cite

Model selection for 24/7 pig position and posture detection by 2D camera imaging and deep learning

Cited by 15 publications

References 11 publications

Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning

Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning

Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

Automated detection and quantification of contact behaviour in pigs using deep learning

Contact Info

Product

Resources

About