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Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods. [ABSTRACT FROM AUTHOR]

Underwater images commonly experience degradation caused by light absorption and scattering in water. Developing lightweight and efficient neural networks to restore degraded images is challenging because of the difficulty in obtaining high-quality paired images and the delicate trade-off between model performance and computational demands. To provide a lightweight and efficient solution for restoring images in terms of color, structure, texture details, etc., enabling the underwater image restoration task to be applied in real-world scenes, we propose an unsupervised lightweight multi-branch context network. Specifically, we design two lightweight multi-branch context subnetworks that enable multiple receptive field feature extraction and long-range dependency modeling to estimate scene radiance and transmission maps. Gaussian blur is adopted to approximate the global background light on the twice-downsampled degraded image. We design a comprehensive loss function that incorporates multiple components, including self-supervised consistency loss and reconstruction loss, to train the network using degraded images in an unsupervised learning manner. Experiments on several underwater image datasets demonstrate that our approach realizes good performance with very few model parameters (0.12 M), and is even comparable to state-of-the-art methods (up to 149 M) in color correction and contrast restoration. [ABSTRACT FROM AUTHOR]