The lane graph is a key component for building high-definition (HD) maps and
crucial for downstream tasks such as autonomous driving or navigation planning.
Previously, He et al. (2022) explored the extraction of the lane-level graph
from aerial imagery utilizing a segmentation based approach. However,
segmentation networks struggle to achieve perfect segmentation masks resulting
in inaccurate lane graph extraction. We explore additional enhancements to
refine this segmentation-based approach and extend it with a diffusion
probabilistic model (DPM) component. This combination further improves the GEO
F1 and TOPO F1 scores, which are crucial indicators of the quality of a lane
graph, in the undirected graph in non-intersection areas. We conduct
experiments on a publicly available dataset, demonstrating that our method
outperforms the previous approach, particularly in enhancing the connectivity
of such a graph, as measured by the TOPO F1 score. Moreover, we perform
ablation studies on the individual components of our method to understand their
contribution and evaluate their effectiveness.

通过改进和扩展分割方法，结合扩散概率模型，提高道路图的质量，特别是在非交叉区域中的连通性，从而优于先前方法。

带有扩散模型的车道分割细化

Lane Segmentation Refinement with Diffusion Models

As airborne vehicles are becoming more autonomous and ubiquitous, it has
become vital to develop the capability to detect the objects in their
surroundings. This paper attempts to address the problem of drones detection
from other flying drones. The erratic movement of the source and target drones,
small size, arbitrary shape, large intensity variations, and occlusion make
this problem quite challenging. In this scenario, region-proposal based methods
are not able to capture sufficient discriminative foreground-background
information. Also, due to the extremely small size and complex motion of the
source and target drones, feature aggregation based methods are unable to
perform well. To handle this, instead of using region-proposal based methods,
we propose to use a two-stage segmentation-based approach employing
spatio-temporal attention cues. During the first stage, given the overlapping
frame regions, detailed contextual information is captured over convolution
feature maps using pyramid pooling. After that pixel and channel-wise attention
is enforced on the feature maps to ensure accurate drone localization. In the
second stage, first stage detections are verified and new probable drone
locations are explored. To discover new drone locations, motion boundaries are
used. This is followed by tracking candidate drone detections for a few frames,
cuboid formation, extraction of the 3D convolution feature map, and drones
detection within each cuboid. The proposed approach is evaluated on two
publicly available drone detection datasets and outperforms several competitive
baselines.

本文提出了使用分割为基础的两阶段方法来检测从其他飞行无人机发射的无人机，通过使用时空注意力线索来确保准确的定位，并以准确性优于其他基线的结果在两个无人机检测数据集上进行了评估。

无人机监测：从视频中识别无人机

Dogfight: Detecting Drones from Drones Videos

Scene text detection has witnessed rapid progress especially with the recent
development of convolutional neural networks. However, there still exists two
challenges which prevent the algorithm into industry applications. On the one
hand, most of the state-of-art algorithms require quadrangle bounding box which
is in-accurate to locate the texts with arbitrary shape. On the other hand, two
text instances which are close to each other may lead to a false detection
which covers both instances. Traditionally, the segmentation-based approach can
relieve the first problem but usually fail to solve the second challenge. To
address these two challenges, in this paper, we propose a novel Progressive
Scale Expansion Network (PSENet), which can precisely detect text instances
with arbitrary shapes. More specifically, PSENet generates the different scale
of kernels for each text instance, and gradually expands the minimal scale
kernel to the text instance with the complete shape. Due to the fact that there
are large geometrical margins among the minimal scale kernels, our method is
effective to split the close text instances, making it easier to use
segmentation-based methods to detect arbitrary-shaped text instances. Extensive
experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the
effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve
texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure
(82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released
in the future.

本文介绍了一种名为 PSENet 的新型逐步缩放扩展网络，能够高精度检测任意形状的场景文字，该方法不仅能够解决任意形状文字的不准确问题，还能有效地解决邻近两个文本集实例合并等问题。