Relative Depth Prediction of Objects in Hazy Images Using Depthmap and Object Detection - B. M., Nandini; Kaulgud, Narasimha (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The ability to establish the relative distances of objects in a single image is essential for many computer vision applications, including scene understanding, augmented reality, and robotics. In this study, we present a method that combines object detection and depth maps to provide an estimate of the relative depth of objects within an image. First, we locate and identify objects within the image using a state-of-the-art object identification model, which yields a set of bounding box coordinates, then estimate the monocular depth maps using a deep learning model. The estimated depth map statistics are used to determine the average depth value of each object enclosed within the bounding box. This data is utilized to estimate the relative distance of objects in the scene. The level of closeness is measured by comparing the average depth value of the objects with a hyper-parameter. An object with average depth value higher than the hyper-parameter is closer to the camera, whereas an object with an average depth value lower than the hyper-parameter is farther away from the camera. We have categorized the relative depths of objects into four levels on the basis of the average depth value’s correlation to the hyper-parameter. Experimental evaluations on standard and real-time datasets have shown that the proposed strategy is effective and precise, emphasizing its potential applicability in several computer vision areas.