https://https://fordatis.fraunhofer.de:443 Fordatis - das institutionelle Forschungsdatenrepositorium der Fraunhofer-Gesellschaft enthält veröffentlichte Forschungsdatensätze der Fraunhofer-Gesellschaft. Diese stehen zur Nachnutzung zur Verfügung. 2026-10-02T07:51:06Z https://fordatis.fraunhofer.de/handle/fordatis/517 Titel: Smart Vibration Sensor Dataset for On-Device Tool Condition Classification in Milling Machines Datenautorinnen und Datenautoren: Bindu Gowri, Akshaya; Liebermann, Joris; Schneider, André; Meyer, Richard Zusammenfassung: Tool wear is a critical factor in milling operations, as increasing tool wear can affect product quality and contribute to increased cutting forces and spindle bearing degradation. This dataset contains vibration measurements acquired for the development and evaluation of an on-device tool condition monitoring system based on TinyML. The data were collected using an Arduino Nano 33 BLE Sense Rev2 development board, equipped with a BMI270 MEMS accelerometer and based on an Arm Cortex-M4 processor. The sensor node was magnetically attached to the spindle housing to capture vibration signals during milling operations. Milling experiments were performed using both a sharp end mill and a visibly worn end mill under nine different process configurations, providing data representing distinct tool conditions and machining conditions. 2026-09-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/515 Titel: Extended Spectrogram Dataset for Deep Learning-Based RF-Frame Detection: Wi-Fi, Bluetooth, and 5G Packets Datenautorinnen und Datenautoren: Bindu Gowri, Akshaya; Wicht, Jakob; Salehi, Hamid Zusammenfassung: Reliable detection of wireless standards in spectrograms is essential for spectrum monitoring, efficient utilization, and interference mitigation. Deep learning-based detectors can localize Radio Frequency (RF) transmissions in time–frequency representations, but require large annotated datasets that are costly to obtain. To develop a robust deep learning model for RF standard identification, we constructed a synthetic dataset of 30,000 spectrograms, extending the dataset generation procedure of Wicht et al. (https://fordatis.fraunhofer.de/handle/fordatis/287). While their dataset included Wi-Fi and Bluetooth signals and their collisions, in this publication we additionally incorporate 5G to better reflect modern wireless environments. 2026-09-16T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/514 Titel: Learning Motion-Aware Representations for World-Space Speed Estimation from Consecutive Frames Datenautorinnen und Datenautoren: Lintao, Fang Zusammenfassung: Object speed estimation is fundamental for understanding dynamic scenes, from traffic monitoring to animal behavior analysis. Existing monocular RGB video approaches typically rely on scene-specific information, such as camera calibration parameters or depth maps, which is often unavailable or unreliable in real-world applications. Further, speed estimation is often decomposed into separate learning problems, such as detection, tracking, depth estimation, geometric projection, and final speed regression, causing errors to accumulate across stages. We therefore propose the Motion-aware Speed Estimation Network (MotiSpeed), a lightweight end-to-end framework that replaces this multi-stage formulation with a single learned model and estimates object-level world-space speed directly from consecutive monocular RGB frames. Evaluating such models, however, requires benchmarks beyond structured planar motion. Existing speed estimation datasets are predominantly vehicle-centric, whereas datasets for unconstrained 3D motion remain limited. Therefore, we introduce AquaSpeed3D, a synthetic underwater dataset for speed estimation with freely moving agents, non-rigid appearance, occlusion, and dense multi-agent interactions. Experiments on AquaSpeed3D, 3DZeF20, and VS13 demonstrate that MotiSpeed achieves competitive performance across underwater and vehicle scenarios while remaining parameter-efficient. 2026-11-26T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/506 Titel: Wheat3DIS: A Benchmark for 3D Instance Segmentation of Wheat Heads from Point Clouds. Datenautorinnen und Datenautoren: Singh, Ashutosh; Hoppe, Sarah; Wree, Philipp Zusammenfassung: 3D computer vision offers powerful tools for efficient agricultural analysis by enabling automated extraction of quantitative traits using point clouds of field scenes. In the context of wheat, accurate analyses of yield-related traits such as spike count, plant density and canopy structure at plot-level and organ morphology such as length, width and volume of wheat heads are essential for high throughput phenotyping. Although recent advances in 3D imaging and point-cloud learning have enabled more detailed analysis of plant architecture, the development of robust wheat phenotyping methods remains limited by the lack of annotated 3D datasets for instance-level segmentation. Existing wheat point-cloud resources primarily focus on part-level or semantic segmentation, where points are assigned to broad categories such as wheat heads, leaves and stems. However, study and analysis of wheat head morphology requires instance-level annotations. In this work, we introduce Wheat3DIS, a multi-source benchmark for 3D wheat head instance segmentation. The benchmark combines in-field wheat point clouds from various sources such as a laser-scanner, a camera array and an iphone. The benchmark provides diverse 3D representations across acquisition modalities, point densities, and reconstruction characteristics. Each scene is annotated with instance-level labels for wheat heads to support the evaluation of wheat organ separation, spike counting, and structure-aware trait extraction. Wheat3DIS provides standardized data splits, annotation protocols, evaluation metrics, and baseline results for representative 3D segmentation architectures. By focusing specifically on wheat and by moving beyond part segmentation toward instance-level understanding, Wheat3DIS establishes a reproducible benchmark for studying 3D wheat perception, cross-sensor generalization, and robust phenotyping under realistic geometric complexity. 2026-01-01T00:00:00Z