This thesis proposes a novel method for instance segmentation in the Robotic Bin-Picking problem using a blend of deep learning and classical methods, titled EdgeNet. Pose estimation is used on sensed data to relay information for usage in object grasping. Industrial bins tend to be incredibly dense, prompting the interest in segmentation with the intent of further usage in pose estimation. Using an encoder- decoder network architecture for estimation of foreground, contours and centroids, followed by a post-processing algorithm, we have achieved performance that is com- petitive with Mask R-CNN while both training and evaluating faster. Furthermore, we have created a benchmark for a new task on the Fraunhofer bin-picking dataset and opened up further research into exploiting instance segmentation to isolate objects prior to pose estimation.