In this paper is presented an approach for fast and accurate segmentation of Deformable Linear Objects (DLOs) named FASTDLO. The perception is obtained from the combination of a deep convolutional neural network for the background segmentation and a pipeline for the dlo identification. The pipeline is based on skeletonization algorithm to highlights the structure of the DLO and a similarity-based network to solve the intersection. FASTDLO is trained only on synthetically generated data, leaving real-data only for evaluation purpose. FASTDLO is experimentally compared against DLO-specific approach achieving better overall performances and a processing rate higher than 20 FPS.