TY  - JOUR
A1  - Camargo, Tibor de
A1  - Schirrmann, Michael
A1  - Landwehr, Niels
A1  - Dammer, Karl-Heinz
A1  - Pflanz, Michael
T1  - Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops
JF  - Remote sensing / Molecular Diversity Preservation International (MDPI)
N2  - Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
KW  - ResNet
KW  - deep residual networks
KW  - UAV imagery
KW  - embedded systems
KW  - crop
KW  - monitoring
KW  - image classification
KW  - site-specific weed management
KW  - real-time mapping
Y1  - 2021
U6  - https://doi.org/10.3390/rs13091704
SN  - 2072-4292
VL  - 13
IS  - 9
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Abdelwahab, Ahmed
A1  - Landwehr, Niels
T1  - Deep Distributional Sequence Embeddings Based on a Wasserstein Loss
JF  - Neural processing letters
N2  - Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
KW  - Metric learning
KW  - Sequence embeddings
KW  - Deep learning
Y1  - 2022
U6  - https://doi.org/10.1007/s11063-022-10784-y
SN  - 1370-4621
SN  - 1573-773X
PB  - Springer
CY  - Dordrecht
ER  -