Instance Segmentation Method of Adherent Targets in Pig Images Based on Improved Mask R-CNN

Zhai, Xinpeng; Tian, Jianyan; Li, Jifu

doi:10.1109/ccdc52312.2021.9601983

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Differences between the data lead to fluctuations up and down in the cross-validation results of the same evaluation indexes. Notably, Cascade-TagLossDetector demonstrates a superior detection segmentation performance in each validation, mainly due to SENet's ability to retain detailed features of the deep semantics, and this conclusion is consistent with Lu Zhou et al's findings [27]. Furthermore, our team's earlier study suggests [28] that the enhanced detection segmentation performance is linked to the utilization of ResNeXt101 as the backbone network, compared to Cascade Mask R-CNN which utilizes ResNet50.…”

Section: Discussionsupporting

confidence: 87%

The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment

Wang,

Fu,

Duan

et al. 2024

Agriculture

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The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs.

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Section: Discussionsupporting

confidence: 87%

The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment

Wang,

Fu,

Duan

et al. 2024

Agriculture

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“…Tu et al obtained an F1 score of 94.05% for individual pig segmentation based on the Mask R-CNN framework (Tu et al, 2020). Zhai et al only used 3000 images to construct the dataset, and the segmentation accuracy reached 88.9% by using the improved Mask R-CNN framework (Zhai et al, 2021). Another study applied DetectorRS (Qiao et al, 2021) to 731 images and achieved a segmentation accuracy of 84.8% (Witte et al, 2021).…”

Section: Segmentationmentioning

confidence: 99%

Recognition and segmentation of individual pigs based on Swin Transformer

Wang

Zhao

et al. 2022

Animal Genetics

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Recognition of individual pigs is critical to the monitoring of pig body size and physiological health status in large-scale pig farms. In this study, deep learning methods were introduced in the intelligent recognition and segmentation of individual replacement pigs, which can realize the non-contact surveillance of each pig. Swin Transformer was used for the recognition and segmentation of individual pigs based on the surveillance data, and different models were compared to find the model with the fastest training speed and most accurate results. Finally, a recognition accuracy of 93.0% and segmentation accuracy of 86.9% for individual pigs were achieved with the surveillance video images of pigs based on Swin Transformer. Even in some complex scenarios such as overlapping, occlusion, and deformation, the method still exhibited excellent recognition performance for replacement pigs. Importantly, this method can greatly save labor as well as help intelligent and unmanned pig production and facilitate the modernization of pig industry.

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“…Hu et al proposed a new dual attention block and introduced it into the Feature Pyramid Network (FPN) to achieve instance segmentation performance with an average precision of 93.1% [22]. Zhai et al used an improved Mask R-CNN to achieve effective segmentation of sticky pigs, with a segmentation accuracy of 91.7% [23]. In addition, in 2022, Liu et al integrated DeepLab V3+, a model with an attention mechanism, and a model for highfrequency and low-frequency feature fusion to build an integrated segmentation model, achieving 76.31% MIoU [24].…”

Section: Introductionmentioning

confidence: 99%

A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs

Wang,

Jiang,

Qiao

et al. 2023

Animals

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This paper proposes a method for automatic pig detection and segmentation using RGB-D data for precision livestock farming. The proposed method combines the enhanced YOLOv5s model with the Res2Net bottleneck structure, resulting in improved fine-grained feature extraction and ultimately enhancing the precision of pig detection and segmentation in 2D images. Additionally, the method facilitates the acquisition of 3D point cloud data of pigs in a simpler and more efficient way by using the pig mask obtained in 2D detection and segmentation and combining it with depth information. To evaluate the effectiveness of the proposed method, two datasets were constructed. The first dataset consists of 5400 images captured in various pig pens under diverse lighting conditions, while the second dataset was obtained from the UK. The experimental results demonstrated that the improved YOLOv5s_Res2Net achieved a mAP@0.5:0.95 of 89.6% and 84.8% for both pig detection and segmentation tasks on our dataset, while achieving a mAP@0.5:0.95 of 93.4% and 89.4% on the Edinburgh pig behaviour dataset. This approach provides valuable insights for improving pig management, conducting welfare assessments, and estimating weight accurately.

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Instance Segmentation Method of Adherent Targets in Pig Images Based on Improved Mask R-CNN

Cited by 3 publications

References 7 publications

The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment

The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment

Recognition and segmentation of individual pigs based on Swin Transformer

A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs

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