A Monocular Camera Depth Estimate Approximation using Deep learning
In numerous applications, such as collision detection, Robotic handling, Robotic-based manufacturing facility, and Advanced Driver Assistance Systems (ADAS), depth estimation is crucial and the most significant task. Radar, Ultrasonic, Lidar technologies both operate by reflecting radio or sound wave or laser beams respectively. Stereo cameras used for depth estimation are costly and increase the cost of the system. There are a few Monocular camera depth estimate approaches that have evolved using mathematical calculation. One approach uses pixel to depth estimate mapping and the other uses the geometry of the road, the contact point of the vehicle on road, and camera properties. The proposed solution would implement depth detection using a monocular camera with deep learning. The objective of the proposed solution is to detect the depth from a monocular image and calibrate it with the actual depth. The pixel to distance data which derived from camera properties is used to run through varied hidden layers and nodes to conclude the implementation using Deep Learning (DL). Evidently, train and test data learn and converge more quickly on the deeper models which has more hidden layers and nodes.
Conference Name: International Conference on Futuristic Technologies (IEEE INCOFT 2022)