Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture

TL;DR

This paper introduces a multi deep CNN architecture that fuses high-level features from Resnet models with faster R-CNN to improve blind-spot collision detection in heavy vehicles, achieving low false detection rates suitable for real-time use.

Contribution

It proposes a novel fusion of two pre-trained Resnet networks with faster R-CNN for enhanced blind-spot vehicle detection in heavy vehicles, outperforming existing methods.

Findings

Fusion of Resnet 50 and Resnet 101 improves detection accuracy.

Achieved false detection rates of 3.05% and 3.49%.

Validated on self-recorded and online datasets.

Abstract

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.