Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Abstract

Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.

A survey on 3D object detection in real time for autonomous driving

“…introduced an unknown-aware hierarchical object detection which incorporates a priori knowledge to distinguish between known classes and unknown classes that could be possible part of a higher taxonomy such as bicycles is a two-wheeled vehicle and vehicle itself is a class. Lastly, a hybrid detector was presented by ( Khan et al, 2023 ) where the YOLO detection head was plugged with the results to RoI pooling stage from a faster R-CNN, thus eliminating the ROI proposals and reducing the computation overhead considerably while improving faster R-CNN results.…”

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

“…Furthermore, the study in [ 23 ] provided an overview of object detection and segmentation systems specific to autonomous vehicles, focusing on the detection methods, sensors, and fusion capabilities to achieve results and extend to related challenges in other application domains. The research paper in [ 24 ] delved into the range of object detection and tracking techniques, emphasising their generalizability in complex settings. The works by Gupta et al [ 22 ] discuss the capabilities of object detection models through the evaluation of object detection metrics from sensor input.…”