Abstract: TV logo recognition is a typical fine object recognition problem,referring to the problem that TV logo region is small and contains low amount of information,hollow-out and translucent logos are easily influenced by background in video frame,a pixel-wise TV logo recognition network based on an end to end fully convolutional network was proposed.Firstly a pixel-wise annotated TV logo dataset was constructed,a TV logo image set was obtained by extracting and preprocessing video frames,and a binary label image set was obtained by proposing a pixel-wise semi-automatic annotation method.Then a pixel-wise TV logo recognition network PNET was proposed based on a typical classification network AlexNet or VGG,and network parameters learned by a classification network in a classification task were converted to the network parameters required by a pixel-wise TV logo recognition network in a segmentation task.Finally a skip architecture was introduced in network combining global information from deep layers and local information from shallow layers.The experiment results show that PNET achieves accurate pixel-wise segmentation.The accuracy is up to 98.3%and inference time for per image on NVIDIA Tesla K80 is less than 1.5 s.