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. 2022 Aug 15;22(16):6088.
doi: 10.3390/s22166088.

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

Muhammad Muzammel  1   2 , Mohd Zuki Yusoff  1 , Mohamad Naufal Mohamad Saad  1 , Faryal Sheikh  3 , Muhammad Ahsan Awais  1
Affiliations

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

Muhammad Muzammel et al. Sensors (Basel). .

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.

Keywords: blind spot collision detection for buses; blind spot vehicle detection; collision detection system; deep CNN architecture; deep learning model; heavy vehicle safety; road safety.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Steps of the proposed approaches to detect blind-spot vehicles using faster R-CNN object detection.
Figure 2
Figure 2
Anchor boxes plot to identify sizes and shapes of different vehicles for faster R-CNN object detection. Each blue circle indicates the label box area versus the label box aspect ratio.
Figure 3
Figure 3
Layer wise integration of proposed models with faster R-CNN for blind-spot vehicle detection.
Figure 4
Figure 4
Proposed 2D CNN architectures to extract deep features for blind-spot vehicle detection.
Figure 5
Figure 5
Pre-trained Resnet 50 and Resnet 101 networks for extracting deep features.
Figure 6
Figure 6
Cameras mounted on the bus mirrors to detect the presence of vehicles in blind spots.
Figure 7
Figure 7
Different types of vehicle detection from self-recorded dataset: (a) three different vehicles in a parallel lane with the bus; (b) one truck in a parallel lane and two cars in the opposite lane; (c) motorcycle at a certain distance; (d) motorcycle very close to the bus.
Figure 8
Figure 8
Vehicle detection from the online LISA dataset: (a) five vehicles detected in dense traffic scenario; (b) six vehicles detected in a dense traffic condition; (c) vehicle detection on highway; (d) vehicle detection on highway; (e) vehicle detection in urban area; and (f) differentiating vehicle and pedestrian.
Figure 8
Figure 8
Vehicle detection from the online LISA dataset: (a) five vehicles detected in dense traffic scenario; (b) six vehicles detected in a dense traffic condition; (c) vehicle detection on highway; (d) vehicle detection on highway; (e) vehicle detection in urban area; and (f) differentiating vehicle and pedestrian.
Figure 9
Figure 9
TPR (%) and FDR (%) analysis of proposed approaches for self-recorded and online LISA datasets.
Figure 10
Figure 10
TPR (%) and FDR (%) analysis of proposed experiments for self-recorded and online LISA datasets.
See this image and copyright information in PMC

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