Object Detection using Image Dehazing: A Journey Of Visual Improvement

Object Detection in hazy conditions is very challenging as haze significantly degrades the visibility of images limits visibility especially in outdoor settings. Here we introduce an interesting method to deal with haze that is present in images. Before applying any object detection method on the hazy input image, it is needed to be dehaze first and recognised later.For dehazing we have used the an Image Dehazing network known as All-in-One Dehazing Network (AOD-net) which is based on reformulation of atmospheric model and generates clean and clear image through a light-weight CNN and for recognition we have used the third version of famous YOLO i.e. YOLOv3. We test our method on various real time hazy images and compare the object similarity results on hazy image as well as on dehaze image. Along with this we have compared the number of object which are recognised in hazy image and in output clear image.