In order to quickly and accurately extract the jet trajectory so that the smart water cannon can automatically adjust the strike direction according to the target movement during the strike,a real-time jet segmentation model based on the mixed dilat-ed convolution MobileNetV2 is proposed.The model uses the lightweight network MobileNetV2 as the basic network,and only uses the first 7 layers of the network to achieve fast real-time segmentation of jet trajectories,a mixed cavity convolution layer is added to collect multi-scale semantic information of different receptive fields to improve jet trajectory segmentation accuracy.Finally,com-bine coding the decoder-decoder structure links the output of the MobileNetV2 network and the output of the mixed-hole convolu-tional layer to further refine the segmentation results.By building a small intelligent water monitor platform and collecting data sets for experiments,the experimental results show that the average intersection ratio of the model reaches 0.781 6,and the average speed of dividing a frame of picture is 19 ms,and it has achieved good performance in segmentation accuracy and segmentation speed.
MobileNetV2dilated convolutionencoder decoderreal time segmentationintelligent water cannon
维普
万方数据
