Parallel channel and position attention-guided feature pyramid for 
pig face posture detection

Hu, Zhiwei; Yan, Hongwen; Lou, Tiantian

doi:10.25165/j.ijabe.20221506.7329

Cited by 5 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose average precision (AP) as the evaluation standard to measure the performance of the model for instance segmentation of group-raised pigs. AP represents the area under the Precision-Recall curve, which can be shown in Formulas (3)- (5), where TP (True Positive) represents the number of pixels correctly predicted as the pig category, FP (False Positive) denotes the number of pixels wrongly predicted as the pig category, and FN (False negative) represents the number of pixels predicted as the background instead of the pig category. Similar to COCO (https://cocodataset.org/) (accessed on 5 April 2023), three IOU thresholds of 0.5, 0.75, 0.5~0.95:0.05 (where 0.05 represents the growth step) were selected to measure the model segmentation performance under different conditions, which were recorded as AP 50 , AP 75 , AP.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Convolutional Neural Network (CNN) has a powerful feature extraction ability for images, and it has been widely used in pig behavior identification [ 1 , 3 ], pig face recognition [ 4 , 5 , 6 , 7 , 8 ], pig multi-target detection [ 9 , 10 ], pig counting [ 11 ], pig detection [ 12 , 13 , 14 ], and other fields [ 15 , 16 ]. In the field of pig image segmentation, for locating sow image segmentation from the overhead views of commercial pens, Yang [ 17 ] first used a fully convolutional network (FCN) to segment the pig, then refined the coarse output of the FCN using the probability map from the final layer of the FCN and Otsu’s thresholding from the hue, saturation, and value color information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Attention-Guided Instance Segmentation for Group-Raised Pigs

Yang

Yan

2023

Animals

View full text Add to dashboard Cite

In the pig farming environment, complex factors such as pig adhesion, occlusion, and changes in body posture pose significant challenges for segmenting multiple target pigs. To address these challenges, this study collected video data using a horizontal angle of view and a non-fixed lens. Specifically, a total of 45 pigs aged 20–105 days in 8 pens were selected as research subjects, resulting in 1917 labeled images. These images were divided into 959 for training, 192 for validation, and 766 for testing. The grouped attention module was employed in the feature pyramid network to fuse the feature maps from deep and shallow layers. The grouped attention module consists of a channel attention branch and a spatial attention branch. The channel attention branch effectively models dependencies between channels to enhance feature mapping between related channels and improve semantic feature representation. The spatial attention branch establishes pixel-level dependencies by applying the response values of all pixels in a single-channel feature map to the target pixel. It further guides the original feature map to filter spatial location information and generate context-related outputs. The grouped attention, along with data augmentation strategies, was incorporated into the Mask R-CNN and Cascade Mask R-CNN task networks to explore their impact on pig segmentation. The experiments showed that introducing data augmentation strategies improved the segmentation performance of the model to a certain extent. Taking Mask-RCNN as an example, under the same experimental conditions, the introduction of data augmentation strategies resulted in improvements of 1.5%, 0.7%, 0.4%, and 0.5% in metrics AP50, AP75, APL, and AP, respectively. Furthermore, our grouped attention module achieved the best performance. For example, compared to the existing attention module CBAM, taking Mask R-CNN as an example, in terms of the metric AP50, AP75, APL, and AP, the grouped attention outperformed 1.0%, 0.3%, 1.1%, and 1.2%, respectively. We further studied the impact of the number of groups in the grouped attention on the final segmentation results. Additionally, visualizations of predictions on third-party data collected using a top-down data acquisition method, which was not involved in the model training, demonstrated that the proposed model in this paper still achieved good segmentation results, proving the transferability and robustness of the grouped attention. Through comprehensive analysis, we found that grouped attention is beneficial for achieving high-precision segmentation of individual pigs in different scenes, ages, and time periods. The research results can provide references for subsequent applications such as pig identification and behavior analysis in mobile settings.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attention-Guided Instance Segmentation for Group-Raised Pigs

Yang

Yan

2023

Animals

View full text Add to dashboard Cite

show abstract

“…As one of the most representative deep learning techniques, convolutional neural networks (CNNs) has powerful feature extraction ability for images, and it has been widely used in classification [6][7][8] , object detection [9][10][11] , image segmentation [12,13] , and other vision tasks [14,15] . In the field of individual pig research, CNNs have been used in areas such as pig counting [16,17] , pig face recognition [18][19][20][21][22] , multi-target tracking [23,24] , pig detection [25][26][27][28] , recognition of the behaviors of pigs [29,30] , and other tasks [31][32][33] . As a type of CNNs, fully convolutional networks (FCNs) [34] are widely used in the field of semantic segmentation and have achieved good performance [35,36] .…”

Section: Introduction mentioning

confidence: 99%

Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation

Hu¹,

Ye²,

Lou³

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The separation of individual pigs from the pigpen scenes is crucial for precision farming, and the technology based on convolutional neural networks can provide a low-cost, non-contact, non-invasive method of pig image segmentation. However, two factors limit the development of this field. On the one hand, the individual pigs are easy to stick together, and the occlusion of debris such as pigpens can easily make the model misjudgment. On the other hand, manual labeling of group-raised pig data is time-consuming and labor-intensive and is prone to labeling errors. Therefore, it is urgent for an individual pig image segmentation model that can perform well in individual scenarios and can be easily migrated to a group-raised environment. In order to solve the above problems, taking individual pigs as research objects, an individual pig image segmentation dataset containing 2066 images was constructed, and a series of algorithms based on fully convolutional networks were proposed to solve the pig image segmentation problem. In order to capture the long-range dependencies and weaken the background information such as pigpens while enhancing the information of individual parts of pigs, the channel and spatial attention blocks were introduced into the best-performing decoders UNet and LinkNet. Experiments show that using ResNext50 as the encoder and Unet as the decoder as the basic model, adding two attention blocks at the same time achieves 98.30% and 96.71% on the F1 and IOU metrics, respectively. Compared with the model adding channel attention block alone, the two metrics are improved by 0.13% and 0.22%, respectively. The experiment of introducing channel and spatial attention alone shows that spatial attention is more effective than channel attention. Taking VGG16-LinkNet as an example, compared with channel attention, spatial attention improves the F1 and IOU metrics by 0.16% and 0.30%, respectively. Furthermore, the heatmap of the feature of different layers of the decoder after adding different attention information proves that with the increase of layers, the boundary of pig image segmentation is clearer. In order to verify the effectiveness of the individual pig image segmentation model in group-raised scenes, the transfer performance of the model is verified in three scenarios of high separation, deep adhesion, and pigpen occlusion. The experiments show that the segmentation results of adding attention information, especially the simultaneous fusion of channel and spatial attention blocks, are more refined and complete. The attention-based individual pig image segmentation model can be effectively transferred to the field of group-raised pigs and can provide a reference for its pre-segmentation.

show abstract

“…However, SegFormer cannot sift features that have the greatest impact on the results and misses the reuse of low-level features, making it difficult to segment cucumber spots with smaller pixel ratios. In recent years, the combined use of the attention mechanism and the Feature Pyramid Network (FPN) has been extensively explored [32][33][34]. The attention mechanism can be used for discriminating feature selection.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Cucumber Leaf Disease Spots Based on ECA-SegFormer

Yang,

Guo,

et al. 2023

Agriculture

View full text Add to dashboard Cite

Accurate semantic segmentation of disease spots is critical in the evaluation and treatment of cucumber leaf damage. To solve the problem of poor segmentation accuracy caused by the imbalanced feature fusion of SegFormer, the Efficient Channel Attention SegFormer (ECA-SegFormer) is proposed to handle the semantic segmentation of cucumber leaf disease spots under natural acquisition conditions. First, the decoder of SegFormer is modified by inserting the Efficient Channel Attention and adopting the Feature Pyramid Network to increase the scale robustness of the feature representation. Then, a cucumber leaf disease dataset is built with 1558 images collected from the outdoor experimental vegetable base, including downy mildew, powdery mildew, target leaf spot, and angular leaf spot. Tested on the dataset, the Mean Pixel Accuracy of ECA-SegFormer is 38.03%, and the mean Intersection over Union is 60.86%, which is 14.55% and 1.47% higher than SegFormer, respectively. These findings demonstrate the superiority of ECA-SegFormer over the original SegFormer, offering enhanced suitability for precise segmentation of cucumber leaf disease spots in the natural environment.

show abstract

Parallel channel and position attention-guided feature pyramid for pig face posture detection

Cited by 5 publications

References 0 publications

Attention-Guided Instance Segmentation for Group-Raised Pigs

Attention-Guided Instance Segmentation for Group-Raised Pigs

Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation

Semantic Segmentation of Cucumber Leaf Disease Spots Based on ECA-SegFormer

Contact Info

Product

Resources

About