5 research outputs found
R-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
Vehicle detection is a significant and challenging task in aerial remote
sensing applications. Most existing methods detect vehicles with regular
rectangle boxes and fail to offer the orientation of vehicles. However, the
orientation information is crucial for several practical applications, such as
the trajectory and motion estimation of vehicles. In this paper, we propose a
novel deep network, called rotatable region-based residual network (R-Net),
to detect multi-oriented vehicles in aerial images and videos. More specially,
R-Net is utilized to generate rotatable rectangular target boxes in a half
coordinate system. First, we use a rotatable region proposal network (R-RPN) to
generate rotatable region of interests (R-RoIs) from feature maps produced by a
deep convolutional neural network. Here, a proposed batch averaging rotatable
anchor (BAR anchor) strategy is applied to initialize the shape of vehicle
candidates. Next, we propose a rotatable detection network (R-DN) for the final
classification and regression of the R-RoIs. In R-DN, a novel rotatable
position sensitive pooling (R-PS pooling) is designed to keep the position and
orientation information simultaneously while downsampling the feature maps of
R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our
network on two open vehicle detection image datasets, namely DLR 3K Munich
Dataset and VEDAI Dataset, demonstrating the high precision and robustness of
our method. In addition, further experiments on aerial videos show the good
generalization capability of the proposed method and its potential for vehicle
tracking in aerial videos. The demo video is available at
https://youtu.be/xCYD-tYudN0
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset
Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa