  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 radiation. 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, as day-time infrared images looks
different than night-time infrared images. Therefore, this study attempts to
alleviate this shortcoming by proposing three different approaches based on
combination of GAN models at two different levels that try 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 has 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.
