The demand for accurate object detection in aerial imagery has surged with
the widespread use of drones and satellite technology. Traditional object
detection models, trained on datasets biased towards large objects, struggle to
perform optimally in aerial scenarios where small, densely clustered objects
are prevalent. To address this challenge, we present an innovative approach
that combines super-resolution and an adapted lightweight YOLOv5 architecture.
We employ a range of datasets, including VisDrone-2023, SeaDroneSee, VEDAI, and
NWPU VHR-10, to evaluate our model's performance. Our Super Resolved YOLOv5
architecture features Transformer encoder blocks, allowing the model to capture
global context and context information, leading to improved detection results,
especially in high-density, occluded conditions. This lightweight model not
only delivers improved accuracy but also ensures efficient resource
utilization, making it well-suited for real-time applications. Our experimental
results demonstrate the model's superior performance in detecting small and
densely clustered objects, underlining the significance of dataset choice and
architectural adaptation for this specific task. In particular, the method
achieves 52.5% mAP on VisDrone, exceeding top prior works. This approach
promises to significantly advance object detection in aerial imagery,
contributing to more accurate and reliable results in a variety of real-world
applications.

通过结合超分辨率和经过修改的轻量级 YOLOv5 架构，我们提出了一种创新的方法，用于在航空影像中准确检测小型密集目标，并通过一系列数据集的评估，证明该模型在高密度遮挡条件下能够实现更好的检测结果，从而为航空影像中的目标检测领域带来显著进展。

从模糊到清晰的检测：基于 YOLOv5 的航空目标检测与超分辨率

From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection  with Super Resolution

Visual representation learning hold great promise for robotics, but is
severely hampered by the scarcity and homogeneity of robotics datasets. Recent
works address this problem by pre-training visual representations on
large-scale but out-of-domain data (e.g., videos of egocentric interactions)
and then transferring them to target robotics tasks. While the field is heavily
focused on developing better pre-training algorithms, we find that dataset
choice is just as important to this paradigm's success. After all, the
representation can only learn the structures or priors present in the
pre-training dataset. To this end, we flip the focus on algorithms, and instead
conduct a dataset centric analysis of robotic pre-training. Our findings call
into question some common wisdom in the field. We observe that traditional
vision datasets (like ImageNet, Kinetics and 100 Days of Hands) are
surprisingly competitive options for visuo-motor representation learning, and
that the pre-training dataset's image distribution matters more than its size.
Finally, we show that common simulation benchmarks are not a reliable proxy for
real world performance and that simple regularization strategies can
dramatically improve real world policy learning.
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通过对机器人的预训练数据集进行集中分析，我们发现常规视觉数据集对于视觉 - 运动表示学习是竞争力强的选择，此外，预训练数据集的图像分布比其规模更为重要，同时简单的正规化策略可以显著改善真实世界策略学习。