Applied Article
H.3.2.2. Computer vision
Homayoun Rastegar; Hassan Khotanlou
Abstract
One of the challenges in digital image processing that we face today is the presence of haze in images. This challenge is particularly prominent in imaging areas with humid and rainy weather compared to other locations. Examples of AI-based systems that can be impacted by this type of challenge include ...
Read More
One of the challenges in digital image processing that we face today is the presence of haze in images. This challenge is particularly prominent in imaging areas with humid and rainy weather compared to other locations. Examples of AI-based systems that can be impacted by this type of challenge include smart traffic control cameras, autonomous vehicles, and Video Assistant Referee (VAR) systems in football stadiums, security and surveillance cameras, and more. Therefore, this paper aims to propose a method that can mitigate this problem using Self-Supervised Learning (SSL) and deep learning. To this end, a Convolutional Autoencoder Network (CAN) with Convolutional Block Attention Module (CBAM) was proposed to reduce haze from images. The advantage of the proposed method is using fewer layers and filters compared to other models introduced by previous researchers in this field and using more important convolutional channels and important image regions using CBAM. Experiments in this paper reveal that overusing large or numerous convolutional filters to generate diverse features can reduce a model's ability to dehaze images effectively. Thus, the number of filters should be carefully limited. On the other hand, a combined loss function was used to train the proposed architecture. The proposed model was trained and tested using NH-haze dataset and the Realistic Single Image Dehazing (RESIDE) dataset. To evaluate our method, we used structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). The test results of the proposed architecture showed that it has higher performance compared to the state-of-the-art in the field.
Original/Review Paper
H.6.5.11. Robotics
Seyedeh Mahsa Zakipour Behambari; Saeed Khankalantary
Abstract
This paper focuses on the design of advanced controllers and the implementation of magnetic tracking and velocity tracking at the position control and formation control levels for a group of quadcopters. Initially, PID controllers are developed based on the quadcopter structure, and then a constrained ...
Read More
This paper focuses on the design of advanced controllers and the implementation of magnetic tracking and velocity tracking at the position control and formation control levels for a group of quadcopters. Initially, PID controllers are developed based on the quadcopter structure, and then a constrained fuzzy-PID controller is introduced to steer the system to the desired position. The performance of this controller is compared with classical PID and fuzzy-PID controllers. This study examines the arrangement and formation coordination of six quadcopters under three different scenarios, evaluating their formation control and coordination. Each quadcopter has an internal controller responsible for maintaining formation accuracy and system stability. Due to the complexity of quadcopter dynamics, trajectory tracking is one of the most challenging research areas. In this regard, a fuzzy-PID controller is proposed to stabilize the quadcopter along predefined trajectories, utilizing speed information as input. Simulation results in the MATLAB/Simulink environment demonstrate that the fuzzy-PID controller outperforms the classical PID controller. Moreover, this controller exhibits greater resistance to external disturbances across all axes, higher accuracy in reducing tracking errors, and improved stability. This superiority is particularly evident in multi-agent systems, emphasizing the significance of advanced control techniques in enhancing the regulation of both single and multi-agent quadcopters. Ultimately, this improves tracking performance while ensuring dynamic efficiency in uncertain environments.
