Chicken Image Segmentation Via Multi-Scale Attention-Based Deep Convolutional Neural Network.

Accurate segmentation and analysis for each animal in surveillance video images will help poultry farmers to monitor and promote animal welfare. However, it is challenging to accurately segment each animal due to the similar appearance, different scales, rapid growth and adhesive areas of group animals. Meanwhile, lacking of useful training data also limits the effectiveness of animal segmentation algorithms. To address these problems, we first construct a chicken image segmentation dataset to study the behavior of chickens for intelligent monitoring and analysis. Then, we propose an effective end-to-end framework for chicken image segmentation, which can also be used for other animal image segmentation. An end-to-end multi-scale based encoder-decoder network is first utilized to extract multi-scale features. Then, an attention-based module is employed to extract and intensify effective features, thus better segmentation results can be obtained. Finally, a multi-output combined loss function is proposed to make effective supervision for better segmentation. Experimental results demonstrate the promising performance of the proposed framework for chicken image segmentation.