Object Detection and Depth Estimation Using Deep Learning

Abstract

Detection of an object and depth estimation is very crucial in the field of computer vision, facilitating tasks in the field of autonomous navigation, scene understanding and many more. There are lot challenges in the current existing technique such as occlusion and accuracy issues, impeding their real-world applicability. To surmount these limitations, the proposed work introduces an innovative approach that melds deep learning architectures with efficient computational methods. By fusing advanced object detection models with a sophisticated depth estimation network, the work proposed have achieved substantial enhancements in accuracy and precision. The proposed model pushes the envelope for real-time implementation, contributing to the advancement of object detection and depth estimation capabilities. This approach was augmented with a novel depth estimation technique, extracting diagonal pixel lengths and combining them with actual depths from the dataset. Subsequent analysis employed both linear and polynomial regression, revealing that the polynomial model (98% average accuracy) surpassed the linear model (80.96% accuracy). These findings highlighted the importance of capturing complex non-linear relationships between pixel length and object depth, showcasing YOLOv4’s robust object detection capabilities and emphasizing the significance of intricate depth estimation in visual cues.