## 1 Introduction  
Robot perception skills have been significantly improved by the use of deep neural networks in image classification, as well as object detection and segmentation [1; 2; 3; 4; 5; 6]. As it is the closest to human vision, segmentation is the most powerful of these and can be applied to a wide range of tasks. Although these advancements are satisfying, they also present many new challenges [7; 8]. Object detection and image segmentation tasks use supervised learning methods that require huge quantities of correctly labelled data to feed the training process. For object detection, a rectangle defines the position of the object in the image. For object segmentation, a set of pixels determines the object silhouette with a mask. To obtain this amount of data, hundreds of hours of monotonous manual work is usually needed. For example, the ImageNet challenge [9] has more than one million images classified into one thousand different categories. Each image has to be labelled into its corresponding category by a human. Moreover, if the classification of image objects is categorized semantically, the effort required to label the objects exponentially increases and risks of bad labelling arise. Object labelling tools include image-sharing options that help offset the tediousness of the manual labelling process. This may enable the outsourcing of the labelling task,with labellers motivated by compensation or personal interest in the subject. Furthermore, image sharing may aid the regulation of the labelling process, allowing further checks of the accuracy of masks and the consistency of labels. It is very important to establish a data quality control system to edit and review object images that have been skipped or badly labelled. Online tools working from browsers help with this task. With an online tool, images can be easily uploaded, and it is therefore easy for a team of people to work on a labelling task. Offline tools require installation before use, and if conducting a team labelling task, they also need cloud storage. Some online and offline labelling tools include multi-user access as a feature for team and project management. This allows co-working between an internal team of experts or managing staff that label the data. Other tools allow the option to automatically track the staff consensus on labels. Many tools also offer labelling metrics that monitor the time taken to label each object and the labelling activity per labeller. This information helps to ascertain labelling costs. To notably reduce the effort required to label the object, some manual object labelling tools propose an objects region with algorithms that infer the boundary of the object to separate the background and the foreground. They use 2D computer vision algorithms for object detection that perform a silhouette analysis, which could help to identify object pixels in the image [10]. However, as obtaining an object silhouette is not an easy task, this method can only be applied in specific situations. Object colour features change depending on where in the image the object is due to illumination. To perform a successful object segmentation that creates a silhouette, it is necessary to control illumination and to have a uniform background. In addition, object colour influences the silhouette detection process as pixel intensity plays an important role in the threshold process. To resolve this problem, some approaches convert images from RGB to HSV colour spaces where object colour is easier to define. Furthermore, with an object contour or silhouette as a region of interest (ROI), it is necessary to extract some scale and time invariant features to decide if it represents an object of interest or not. For example, to detect objects in images, convex defect detection measures the ratios between convex hull areas and object silhouette areas [11]. To resolve the problems with illumination, object colour changes and controlled backgrounds, techniques that focus on detection of edges have arisen.