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3 Annals of Computer Science and Information Systems

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Enhancement of Images Taken in Fog Condition: A Review

100%

Das R. , Rajkonwar T. , Bora D. j.

tom Vol. 24

5--9

Images captured using camera systems in foggy weather conditions often suffer from poor visibility and can be seriously degraded due to atmospheric conditions, which creates a lot of impacts on the outdoor computer vision systems. To solve this problem, image enhancement is very important as this process is used for enhancing the quality of an image, and for this purpose numerous visibility enhancement techniques have been used and applied. In this paper, we have tried to describe how to enhance an image using different techniques and methods. For this, various techniques and methods have been studied for image enhancement used in different research and review papers. The main goal of this paper is to understanding and reviewing the techniques used for image enhancement.

Applying Knowledge Distillation to Improve Weed Mapping With Drones

67%

Castellano G. , De Marinis P. , Vessio G.

tom Vol. 35

393--400

Non-invasive remote sensing using UAVs can be used in precision agriculture to observe crops in visible and non-visible spectra. This paper investigates the effectiveness of state-of-the-art knowledge distillation techniques for mapping weeds with drones, an essential component of precision agriculture that employs remote sensing to monitor crops and weeds. The study introduces a lightweight Vision Transformer-based model that achieves optimal weed mapping capabilities while maintaining minimal computation time. The research shows that the student model effectively learns from the teacher model using the WeedMap dataset, achieving accurate results suitable for mobile platforms such as drones, with only 0.5 GMacs compared to 42.5 GMacs of the teacher model. The trained models obtained an F1 score of 0.863 and 0.631 on two data subsets, with a performance improvement of 2 and 7 points, respectively, over the undistilled model. The study results suggest that developing efficient computer vision algorithms on drones can significantly improve agricultural management practices, leading to greater profitability and environmental sustainability.

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

67%

Ludziejewski J. , Grad Ł. , Przebinda Ł. , Tajmajer T.

tom Vol. 21

105--114

Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from real-life test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their owners, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.