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

Muhammad Muzammel^1,2, Mohd Zuki Yusoff¹, Mohamad Naufal Mohamad Saad¹

¹Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

Sensors (Basel, Switzerland)

|August 26, 2022

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This summary is machine-generated.

Heavy vehicle blind-spot collisions can be detected using new vision-based object detection methods. Integrating multiple neural networks significantly improves detection accuracy, enhancing road safety for vulnerable road users.

Area of Science:

Computer Vision
Artificial Intelligence
Road Safety Engineering

Background:

Heavy vehicles possess larger blind spots than cars, increasing the risk of severe accidents.
Current vision-based object detection models often rely on single feature descriptors, limiting their effectiveness.

Purpose of the Study:

To propose novel convolutional neural network (CNN) designs for detecting blind-spot collisions in heavy vehicles.
To enhance object detection by integrating high-level feature descriptors and a fusion approach.

Main Methods:

Developed two CNNs utilizing high-level feature descriptors and integrated them with the faster R-CNN framework.
Implemented a fusion approach combining pre-trained Resnet 50 and Resnet 101 networks for feature extraction.
Validated the models on a custom bus blind-spot dataset and the public LISA dataset.

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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Main Results:

The proposed fusion approach significantly improved the performance of faster R-CNN for blind-spot detection.
Achieved low false detection rates (3.05% and 3.49%) on the custom dataset, demonstrating suitability for real-time applications.
Outperformed existing state-of-the-art methods in blind-spot vehicle detection.

Conclusions:

The integrated CNN and feature fusion approach offers a robust solution for real-time blind-spot collision detection in heavy vehicles.
This advancement has the potential to significantly reduce accidents and improve safety for pedestrians and cyclists around large vehicles.