Cutting-Edge Deep Learning Methods for Image-Based Object Detection in Autonomous Driving: In-Depth Survey

Abstract

Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image-based object detection methods, which offer several advantages over other modalities, such as cost-effectiveness and the ability to capture visual features like colour and texture that are not detectable by LiDAR. We provide a comprehensive survey of DL-based strategies for detecting vehicles and pedestrians using 2D images, analysing both one-stage and two-stage detection frameworks. Additionally, we review the most commonly used publicly available datasets in autonomous driving research and highlight their relevance to 2D detection tasks. The paper concludes by discussing the current challenges in this domain and proposing potential future directions, aiming to bridge the gap between the capabilities of 2D image-based models and the requirements of real-world autonomous driving applications. Comparative tables are included to facilitate a clear understanding of the different approaches and datasets.