33 research outputs found
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos
Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new
frontier of computer vision that requires automated analysis of aerial
images/videos. However, the existing UAV datasets primarily focus on object
detection. An object detector does not differentiate between the moving and
non-moving objects. Given a real-time UAV video stream, how can we both
localize and classify the moving objects, i.e. perform moving object
recognition (MOR)? The MOR is one of the essential tasks to support various UAV
vision-based applications including aerial surveillance, search and rescue,
event recognition, urban and rural scene understanding.To the best of our
knowledge, no labeled dataset is available for MOR evaluation in UAV videos.
Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for
MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes
for moving objects which requires less computational resources than producing
pixel-level estimates. We annotate 89,783 moving object instances collected
from 30 UAV videos, consisting of 10,948 frames in various scenarios such as
weather conditions, occlusion, changing flying altitude and multiple camera
views. We assigned the labels for two categories of vehicles (car and heavy
vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR
in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present
16 baseline results based on the proposed framework over the MOR-UAV dataset
through quantitative and qualitative experiments. We also analyze the
motion-salient regions in the network through multiple layer visualizations.
The MOR-UAVNet works online at inference as it requires only few past frames.
Moreover, it doesn't require predefined target initialization from user.
Experiments also demonstrate that the MOR-UAV dataset is quite challenging
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
With the advancement of maritime unmanned aerial vehicles (UAVs) and deep
learning technologies, the application of UAV-based object detection has become
increasingly significant in the fields of maritime industry and ocean
engineering. Endowed with intelligent sensing capabilities, the maritime UAVs
enable effective and efficient maritime surveillance. To further promote the
development of maritime UAV-based object detection, this paper provides a
comprehensive review of challenges, relative methods, and UAV aerial datasets.
Specifically, in this work, we first briefly summarize four challenges for
object detection on maritime UAVs, i.e., object feature diversity, device
limitation, maritime environment variability, and dataset scarcity. We then
focus on computational methods to improve maritime UAV-based object detection
performance in terms of scale-aware, small object detection, view-aware,
rotated object detection, lightweight methods, and others. Next, we review the
UAV aerial image/video datasets and propose a maritime UAV aerial dataset named
MS2ship for ship detection. Furthermore, we conduct a series of experiments to
present the performance evaluation and robustness analysis of object detection
methods on maritime datasets. Eventually, we give the discussion and outlook on
future works for maritime UAV-based object detection. The MS2ship dataset is
available at
\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs
Mixed-precision neural networks (MPNNs) that enable the use of just enough
data width for a deep learning task promise significant advantages of both
inference accuracy and computing overhead. FPGAs with fine-grained
reconfiguration capability can adapt the processing with distinct data width
and models, and hence, can theoretically unleash the potential of MPNNs.
Nevertheless, commodity DPUs on FPGAs mostly emphasize generality and have
limited support for MPNNs especially the ones with lower data width. In
addition, primitive DSPs in FPGAs usually have much larger data width than that
is required by MPNNs and haven't been sufficiently co-explored with MPNNs yet.
To this end, we propose an open source MPNN accelerator design framework
specifically tailored for FPGAs. In this framework, we have a systematic
DSP-packing algorithm to pack multiple lower data width MACs in a single
primitive DSP and enable efficient implementation of MPNNs. Meanwhile, we take
DSP packing efficiency into consideration with MPNN quantization within a
unified neural network architecture search (NAS) framework such that it can be
aware of the DSP overhead during quantization and optimize the MPNN performance
and accuracy concurrently. Finally, we have the optimized MPNN fine-tuned to a
fully pipelined neural network accelerator template based on HLS and make best
use of available resources for higher performance. Our experiments reveal the
resulting accelerators produced by the proposed framework can achieve
overwhelming advantages in terms of performance, resource utilization, and
inference accuracy for MPNNs when compared with both handcrafted counterparts
and prior hardware-aware neural network accelerators on FPGAs.Comment: Accepted by 2023 IEEE/ACM International Conference on Computer-Aided
Design (ICCAD