45 research outputs found
Enhancing vehicle detection accuracy in thermal infrared images using multiple GANs
Vehicle detection accuracy is fairly accurate in good-illumination conditions
but susceptible to poor detection accuracy under low-light conditions. The
combined effect of low-light and glare from vehicle headlight or tail-light
results in misses in vehicle detection more likely by state-of-the-art object
detection models. However, thermal infrared images are robust to illumination
changes and are based on thermal radiations. Recently, Generative Adversarial
Networks (GANs) have been extensively used in image domain transfer tasks.
State-of-the-art GAN models have attempted to improve vehicle detection
accuracy in night-time by converting infrared images to day-time RGB images.
However, these models have been found to under-perform during night-time
conditions compared to day-time conditions. Therefore, this study attempts to
alleviate this shortcoming by proposing three different approaches based on
combination of GAN models at two different levels that tries to reduce the
feature distribution gap between day-time and night-time infrared images.
Quantitative analysis to compare the performance of the proposed models with
the state-of-the-art models have been done by testing the models using
state-of-the-art object detection models. Both the quantitative and qualitative
analyses have shown that the proposed models outperform the state-of-the-art
GAN models for vehicle detection in night-time conditions, showing the efficacy
of the proposed models
An Integrated Enhancement Solution for 24-hour Colorful Imaging
The current industry practice for 24-hour outdoor imaging is to use a silicon
camera supplemented with near-infrared (NIR) illumination. This will result in
color images with poor contrast at daytime and absence of chrominance at
nighttime. For this dilemma, all existing solutions try to capture RGB and NIR
images separately. However, they need additional hardware support and suffer
from various drawbacks, including short service life, high price, specific
usage scenario, etc. In this paper, we propose a novel and integrated
enhancement solution that produces clear color images, whether at abundant
sunlight daytime or extremely low-light nighttime. Our key idea is to separate
the VIS and NIR information from mixed signals, and enhance the VIS signal
adaptively with the NIR signal as assistance. To this end, we build an optical
system to collect a new VIS-NIR-MIX dataset and present a physically meaningful
image processing algorithm based on CNN. Extensive experiments show outstanding
results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral