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%.
