|
| Research on Image Segmentation Algorithms on Broiler Depth Atlas |
| |
| DOI：10.16768/j.issn.1004-874X.2022.01.019 |
|
| Hits: 1438 |
| Download times: 992 |
| Abstract: |
| 【Objective】Aiming at the difficulty in recognizing and segmenting multiple broilers in complex
environment, a segmentation method for depth map of multiple broilers based on deep learning was explored.
【Method】By using the depth camera, the depth map of broilers in different postures（standing, prone, looking
up, looking down, etc.）were collected in the natural environment through different shooting angles（top, front
and side）, and the depth map were accurately marked by CVAT labeling software. A broiler depth map dataset
was established, with a total of 4 058 depth maps. Five neural networks, including FCN, U-NET, PSPNet,
DeepLab and Mask R-CNN, were used to recognize and segment broiler depth maps. Based on the predicted
results of test sets, the performance of different models were compared and evaluated to realize the recognition
and segmentation of broiler depth maps.【Result】The recognition and segmentation accuracy of Mask R-CNN
neural network model is 98.96%, the recall rate is 97.78%, the F1
score is 95.03%, and the intersection-overunion is 94.69%, all of which are the optimal values of the five models.【Conclusion】The algorithm based
on Mask R-CNN is simple and fast, and it can realize the automatic recognition and segmentation of broilers
accurately and has good robustness to the shielding of broilers, which can basically meet the recognition and segmentation requirements for the prediction of the evenness of chicken flocks in the chicken farm. It promotes the
application of computer vision in modern agriculture, and provides theoretical and practical bases for chicken farm
operations such as flock counting, flock evenness prediction and welfare breeding of broilers. |
|
View Full Text
View/Add Comment Download reader |
|
|
|
