60 research outputs found
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
Most existing domain adaptation (DA) methods align the features based on the
domain feature distributions and ignore aspects related to fog, background and
target objects, rendering suboptimal performance. In our DA framework, we
retain the depth and background information during the domain feature
alignment. A consistency loss between the generated depth and fog transmission
map is introduced to strengthen the retention of the depth information in the
aligned features. To address false object features potentially generated during
the DA process, we propose an encoder-decoder framework to reconstruct the
fog-free background image. This reconstruction loss also reinforces the
encoder, i.e., our DA backbone, to minimize false object features.Moreover, we
involve our target data in training both our DA module and our detection module
in a semi-supervised manner, so that our detection module is also exposed to
the unlabeled target data, the type of data used in the testing stage. Using
these ideas, our method significantly outperforms the state-of-the-art method
(47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains
the best performance on multiple real-image public datasets. Code is available
at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-ScenesComment: Accepted by ACC
Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions
Visual perception is an important component for autonomous navigation of
unmanned surface vessels (USV), particularly for the tasks related to
autonomous inspection and tracking. These tasks involve vision-based navigation
techniques to identify the target for navigation. Reduced visibility under
extreme weather conditions in marine environments makes it difficult for
vision-based approaches to work properly. To overcome these issues, this paper
presents an autonomous vision-based navigation framework for tracking target
objects in extreme marine conditions. The proposed framework consists of an
integrated perception pipeline that uses a generative adversarial network (GAN)
to remove noise and highlight the object features before passing them to the
object detector (i.e., YOLOv5). The detected visual features are then used by
the USV to track the target. The proposed framework has been thoroughly tested
in simulation under extremely reduced visibility due to sandstorms and fog. The
results are compared with state-of-the-art de-hazing methods across the
benchmarked MBZIRC simulation dataset, on which the proposed scheme has
outperformed the existing methods across various metrics.Comment: IEEE/RSJ International Conference on Intelligent Robots (IROS-2023
UDP-YOLO: High Efficiency and Real-Time Performance of Autonomous Driving Technology
In recent years, autonomous driving technology has gradually appeared in our field of vision. It senses the surrounding environment by using radar, laser, ultrasound, GPS, computer vision and other technologies, and then identifies obstacles and various signboards, and plans a suitable path to control the driving of vehicles. However, some problems occur when this technology is applied in foggy environment, such as the low probability of recognizing objects, or the fact that some objects cannot be recognized because the fog's fuzzy degree makes the planned path wrong. In view of this defect, and considering that automatic driving technology needs to respond quickly to objects when driving, this paper extends the prior defogging algorithm of dark channel, and proposes UDP-YOLO network to apply it to automatic driving technology. This paper is mainly divided into two parts: 1. Image processing: firstly, the data set is discriminated whether there is fog or not, then the fogged data set is defogged by defogging algorithm, and finally, the defogged data set is subjected to adaptive brightness enhancement; 2. Target detection: UDP-YOLO network proposed in this paper is used to detect the defogged data set. Through the observation results, it is found that the performance of the model proposed in this paper has been greatly improved while balancing the speed
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