Image dehazing techniques can play a vital role in object detection,
surveillance, and accident prevention, especially in scenarios where visibility
is compromised because of light scattering by atmospheric particles. To obtain a
high-quality image or as an initial step in processing, it’s crucial to restore
the scene’s information from a single image, given that this is an ill-posed
inverse problem. The present approach utilized an unsupervised learning approach
to predict the transmission map from a hazy image and used YOLOv8n to detect the
car from a clear recovered image. The dehazing model utilized a lightweight
parallel channel architecture to extract features from the input image and
estimate the transmission map. The clear image is recovered using an atmospheric
scattering model and given to the YOLOv8n for car detection. By incorporating
dark channel prior loss during training, the model eliminates the need for a
paired dataset. The proposed dehazing model with fewer parameters speeds up the
dehazing process, which can detect the objects in less response time. The
network follows unsupervised learning, which eliminates the need of ground truth
image or transmission map of a clear image. The proposed method tried to solve
the issue of high computational complexity and long latency when used as a
preprocessing stage in computer vision applications. The proposed network ranks
first in terms of parameters and FLOPs, which are lower by scale 102
and 103, respectively, compared to the method ranked second. The
results highlight the effectiveness of the proposed method compared to other
methods and ranked first in number of car detections using YOLOv8n. The
inference time to dehaze the image is comparable to the method ranked first and
66% lower than the third rank.
