6 research outputs found
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields
Perception datasets for agriculture are limited both in quantity and
diversity which hinders effective training of supervised learning approaches.
Self-supervised learning techniques alleviate this problem, however, existing
methods are not optimized for dense prediction tasks in agriculture domains
which results in degraded performance. In this work, we address this limitation
with our proposed Injected Noise Discriminator (INoD) which exploits principles
of feature replacement and dataset discrimination for self-supervised
representation learning. INoD interleaves feature maps from two disjoint
datasets during their convolutional encoding and predicts the dataset
affiliation of the resultant feature map as a pretext task. Our approach
enables the network to learn unequivocal representations of objects seen in one
dataset while observing them in conjunction with similar features from the
disjoint dataset. This allows the network to reason about higher-level
semantics of the entailed objects, thus improving its performance on various
downstream tasks. Additionally, we introduce the novel Fraunhofer Potato 2022
dataset consisting of over 16,800 images for object detection in potato fields.
Extensive evaluations of our proposed INoD pretraining strategy for the tasks
of object detection, semantic segmentation, and instance segmentation on the
Sugar Beets 2016 and our potato dataset demonstrate that it achieves
state-of-the-art performance.Comment: 8 pages, 7 figure
A Smart Robotic System for Industrial Plant Supervision
In today's chemical plants, human field operators perform frequent integrity
checks to guarantee high safety standards, and thus are possibly the first to
encounter dangerous operating conditions. To alleviate their task, we present a
system consisting of an autonomously navigating robot integrated with various
sensors and intelligent data processing. It is able to detect methane leaks and
estimate its flow rate, detect more general gas anomalies, recognize oil films,
localize sound sources and detect failure cases, map the environment in 3D, and
navigate autonomously, employing recognition and avoidance of dynamic
obstacles. We evaluate our system at a wastewater facility in full working
conditions. Our results demonstrate that the system is able to robustly
navigate the plant and provide useful information about critical operating
conditions.Comment: Final submission for IEEE Sensors 202
Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling
We present a multi-sensor system for consistent 3D hand pose tracking and modeling that leverages the advantages of both wearable and optical sensors. Specifically, we employ a stretch-sensing soft glove and three IMUs in combination with an RGB-D camera. Different sensor modalities are fused based on the availability and confidence estimation, enabling seamless hand tracking in challenging environments with partial or even complete occlusion. To maximize the accuracy while maintaining high ease-of-use, we propose an automated user calibration that uses the RGB-D camera data to refine both the glove mapping model and the multi-IMU system parameters. Extensive experiments show that our setup outperforms the wearable-only approaches when the hand is in the field-of-view and outplays the camera-only methods when the hand is occluded.ISSN:1077-2626ISSN:1941-0506ISSN:2160-930