Semantic Segmentation of Tennis Scene Based on Series Atrous Convolution Neural Network

Semantic segmentation of outdoor tennis scene is a key technology for developing mobile robot to service on tennis court.The outdoor tennis court environment contains dynamic illumination,perspective and other interference factors,which make it difficult to carry out semantic segmentation.To solve this problem series atrous convolution neural network(SACNet)is proposed based on Deeplabv3.SACNet extends the multi-scale module of atrous convolution to enlarge the receptive field of feature map in training by means of odd-even mixed dilation rate.SACNet reduces its space-time complexity by means of grouping convolution.To overcome the problem of over-fitting caused by limited training samples,the pre-trained residual neural network ResNet-50 is used for transfer learning by freezing the parameters of batch normalization layer.Experimental results on the self-made tennis court datasets show that SACNet improves the test mIOU compared with Deeplabv3 by 10.22%and is more accurate for tennis court boundaries and small targets.