Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks

Abstract:

Object recognition using Unmanned Aerial Vehicles (UAVs) is increasingly becoming

more useful. Tremendous success has been achieved on UAV object recognition in

clear weather conditions where adequate illumination makes it easier for UAVs to

recognize objects in the scene. Unfortunately, for outdoor applications, there is no

escape from bad weather moments such as haze, fog, dust, smoke and smog. These

weather nuisances occur due to suspended particles in the atmosphere, ultimately

resulting in degraded visibility. Thus, these weather nuisances cause unsatisfactory

performance in UAV object recognition. Current UAV object recognition algorithms

do not guarantee satisfactory performance in bad weather conditions. Therefore, this

study was motivated by the need for UAV object recognition systems that can perform

robustly despite the state of the weather.

Several state-of-the-art methods exist for object recognition and image

dehazing/defogging. Nonetheless, the performance of these methods is dependent on

the scenarios where they are used. In this study, a novel method that deployed the Dark

Channel Prior (DCP), for scene dehazing/defogging; and Convolutional Neural

Network (CNN) for object recognition; was proposed and investigated in the context of

UAV for object recognition in bad weather.

The aim of the study was to investigate the proposed method for enabling the UAV to

efficiently recognize objects in bad weather conditions such as fog, haze, smoke and

smog. The proposed method was experimented to determine the extend at which it

can enable the UAV recognize objects in fog/haze weather. The objective of the

experiments was to investigate the performance of the proposed method for addressing

UAV object recognition in bad weather by observing two independent variables,

namely; (1) fog density, which is the measure of fog present in the scene and (2)

distance of object from the UAV, in fog.

Analysis of results demonstrated that the DCP method effectively addresses UAV

visibility improvement in bad weather conditions. On varied densities of haze/fog, the

DCP method enables the UAV to effectively dehaze/defog scenes and improve

visibility of objects present in the scene. Additionally, analysis of results illustrated that

the constructed CNN model can enable the UAV to accurately recognize objects from

the dehazed/defogged scenes with a confidence accuracy of 94.3%.

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APA

Kaloso, T (2024). Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks. Afribary. Retrieved from https://afribary.com/works/unmanned-aerial-vehicle-object-recognition-in-bad-weather-using-dark-channel-prior-and-convolutional-neural-networks

MLA 8th

Kaloso, Topias "Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks" Afribary. Afribary, 30 Mar. 2024, https://afribary.com/works/unmanned-aerial-vehicle-object-recognition-in-bad-weather-using-dark-channel-prior-and-convolutional-neural-networks. Accessed 21 May. 2024.

MLA7

Kaloso, Topias . "Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks". Afribary, Afribary, 30 Mar. 2024. Web. 21 May. 2024. < https://afribary.com/works/unmanned-aerial-vehicle-object-recognition-in-bad-weather-using-dark-channel-prior-and-convolutional-neural-networks >.

Chicago

Kaloso, Topias . "Unmanned aerial vehicle object recognition in bad weather using dark channel prior and convolutional neural networks" Afribary (2024). Accessed May 21, 2024. https://afribary.com/works/unmanned-aerial-vehicle-object-recognition-in-bad-weather-using-dark-channel-prior-and-convolutional-neural-networks