Deep Learning-Based Approach for Efficient 3D Object Detection in Autonomous Vehicles

The YOLOv3 deep learning architecture is utilized in this project to develop an advanced framework for 3D object detection in vehicle automation. This technology ensures precise and rapid detection of essential objects like cars, pedestrians, and barriers, crucial for safe autonomous driving. By integrating 2D bounding boxes with 3D coordinate estimations, the system analyzes input from multiple sensors to identify and classify various elements in the driving environment. This capability is vital for navigating complex scenarios that require swift decision-making, particularly in urban settings, highways, and unpredictable traffic. A primary objective is to enhance computational efficiency for real-time applications, achieving an impressive 89.55% detection accuracy. Performance evaluations demonstrate the system’s adaptability across diverse conditions, including varying lighting, weather, and road types. The combination of 3D object recognition and rapid processing positions this framework as a key advancement in developing reliable and secure automated vehicles.

I. Introduction

The rapid growth of autonomous technology has made it easier to investigate dependable and efficient 3D object identification systems. These systems are required for safe navigation in tough environments, as well as observation and inference. Object detection is a critical component of any autonomous driving system since it allows an autonomous vehicle to identify and track items in real time, such as pedestrians, other vehicles, and barriers. YOLOv3, with its extraordinary real-time speed and accuracy, appears to be one of the most popular object identification systems ever built. YOLOv3 is a famous one-shot detector that uses The CNNbased image uses bounding box position and class probabilities as prediction targets. It has been demonstrated to outperform traditional multi-stage object detection algorithms, which rely on multi-pass methods for initial picture identification and then object identification. YOLOv3’s structure is complicated by the addition of extra convolutional blocks, batch normalisation, and skip connections with leaky ReLU. This improves the model’s ability to intra-clip spatial information from the input image. The upgraded version of YOLOv3, a new mathematical framework designed exclusively for autonomous vehicles to target 3-D objects, allows multi-phase detection algorithms to differentiate targets of varying sizes. This deduction has numerous levels [1] –[3].