Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
- PDF / 772,269 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 17 Downloads / 194 Views
Regular Article - Experimental Physics
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data Jan Kieselera CERN, Experimental Physics Department, Geneva, Switzerland
Received: 15 April 2020 / Accepted: 10 September 2020 © The Author(s) 2020
Abstract High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis. Traditional approaches from computer vision either impose implicit constraints on the object size or density and are not well suited for sparse detector data or rely on objects being dense and solid. The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals. The pixels or vertices themselves serve as representations of the entire object, and a combination of learnable local clustering in a latent space and confidence assignment allows one to collect condensates of the predicted object properties with a simple algorithm. As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals. The latter results are also compared to a classic particle flow approach.
1 Introduction Accurately detecting a large number of objects belonging to a variety of classes within the same image has triggered very successful developments of deep neural network architectures and training methods [1–7]. Among these are two-stage detectors, where a first stage generates a set of candidate proposals, comparable to seeds, and in a second stage the object properties are determined. Even though twostage approaches yield high accuracy, they are very resource demanding and comparably slow. One-stage architectures, a e-mail:
however, have proven to be just as powerful but with significantly lower resource requirements [5,8–11]. Many oneand two-stage detectors use a grid of anchor boxes to attach object proposals directly to the anchors corresponding to the object in question. Ambiguities are usually resolved in a second step by evaluating the intersection over union score of the bounding boxes [12]. Recent anchor free approaches identify key points instead of using anchor boxes, which are tightly coupled to the centre of the object [9,10]. Reconstructing and identifying objects (e.g. particles) from detector hits in e.g. a high-energy physics experiment are, in principle, similar tasks, in the sense that both rely on a finely grained set of individual inputs (e.g. pixels or detector hits) and infer higher-level object properties from them. However, a detector is m
Data Loading...
