Mounting Behaviour Recognition for Pigs Based on Deep Learning

Li, Dan; Chen, Yifei; Zhang, Kaifeng; Li, Zhenbo

doi:10.3390/s19224924

Cited by 52 publications

(38 citation statements)

References 22 publications

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“…Real-time sow drinking, urination, and mounting behavior recognition has been achieved by using an optimized target detection method based on the SSD and the MobileNet [24]. Li et al [10] proposed Mask R-CNN to segment pigs from images and then extracted the eigenvectors for mounting recognition. These methods extracted spatial features from still images without considering the temporal features of behavior.…”

Section: Feeding Scratching Mounting Lying Walkingmentioning

confidence: 99%

“…Estrus can be estimated through pig mounting behavior [7], which can cause bruises, lameness, stress, and leg fractures [8,9]. So, the timely detection and intervention of mounting behavior can increase animal welfare and further ensure pig health [10]. Pig scratching behavior is the grooming behavior whose function is mainly to reduce or eliminate external stimuli such as parasites, flies, mosquitoes, and dirt.…”

Section: Introductionmentioning

confidence: 99%

“…Real-time recognition speed could reach 7 frames per second, and the mAP was 93.4%. Mask Region-Convolutional Neural Networks (Mask R-CNN) was also used as a pig segmentation network [10]. Then, the eigenvectors, which included the region of interest (RoI) parameters and mask coordinates, could be extracted.…”

Section: Introductionmentioning

confidence: 99%

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Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

Zhang¹,

Li²,

Huang³

et al. 2020

Sensors

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The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.

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Section: Feeding Scratching Mounting Lying Walkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

Zhang¹,

Li²,

Huang³

et al. 2020

Sensors

Self Cite

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“…The pixel distance of the pig head in drinking water area and the water pipe was calculated for recognizing drinking behavior [4]. Mask region-convolutional neural networks (Mask R-CNN) and kernel-extreme learning were used for detecting mounting behavior [5]. In addition to algorithms for detecting single behavior, there are algorithms for classifying multiple behaviors as well.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs

Zhang

et al. 2020

Sensors

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The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig's behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig's multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.

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“…In recent years, animal imaging analysis or phenotyping has been tested to monitor animal behavior and health on dairy farms [3][4][5][6][7]. The behaviors of dairy cattle reflect their physiological and welfare conditions, and thus can be applied to improve our understanding of farm animal production, health, and welfare [8].…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf

Guo

Chai

2020

Animals

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Requirements for animal and dairy products are increasing gradually in emerging economic bodies. However, it is critical and challenging to maintain the health and welfare of the increasing population of dairy cattle, especially the dairy calf (up to 20% mortality in China). Animal behaviors reflect considerable information and are used to estimate animal health and welfare. In recent years, machine vision-based methods have been applied to monitor animal behaviors worldwide. Collected image or video information containing animal behaviors can be analyzed with computer languages to estimate animal welfare or health indicators. In this proposed study, a new deep learning method (i.e., an integration of background-subtraction and inter-frame difference) was developed for automatically recognizing dairy calf scene-interactive behaviors (e.g., entering or leaving the resting area, and stationary and turning behaviors in the inlet and outlet area of the resting area) based on computer vision-based technology. Results show that the recognition success rates for the calf’s science-interactive behaviors of pen entering, pen leaving, staying (standing or laying static behavior), and turning were 94.38%, 92.86%, 96.85%, and 93.51%, respectively. The recognition success rates for feeding and drinking were 79.69% and 81.73%, respectively. This newly developed method provides a basis for inventing evaluation tools to monitor calves’ health and welfare on dairy farms.

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Mounting Behaviour Recognition for Pigs Based on Deep Learning

Cited by 52 publications

References 22 publications

Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks

A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf

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