# Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation Source: [Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:55:10.976Z Authors: Stefan Hoermann, Philipp Henzler, Martin Bach, Klaus Dietmayer Published: 2018-01-30T08:18:31.000Z ## Abstract We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360{\\deg} coverage were fused in a dynamic occupancy grid map (DOGMa). A single-stage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9% ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()