Research on Image Segmentation Algorithms on Broiler Depth Atlas

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-over-union 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.

Keywords

• image recognition
• image segmentation
• deep learning
• depth map
• neural network model