Monitoring of Egg Growing in Video by the Improved DeepLabv3+ Network Model

The paper proposes the noninvasive image egg growing monitoring method based on an illumination and transfer learning. During the egg growing, the size of egg air cell is increased. The segmentation is performed to extract cells and segmentation parameters are adjusted and trained on an air cell datasets by transfer learning to separate air cells with high light transmittance from the background. The improved DeepLabV3+ network model for image egg monitoring is proposed. The network embeds coordinate attention in the lightweight network MobilenetV2. The decoder feature fusion method is improved to a semantic embedding branch structure. The middle-level features that have been newly introduced are merged with the high-level features and low-level features. The results show that the mean intersection over union of the model reaches 89.06% and that the mean pixel accuracy rate reaches 94.66%. The method can effectively segment the air cell part of the eggs. The feasibility of the method was verified by measuring the air cells of egg growing process from the 7th to the 19th day.