Monocular Cyclist Detection with Convolutional Neural Networks

TL;DR

This paper presents a real-time monocular cyclist detection system using convolutional neural networks, achieving high accuracy and fast inference, with potential to enhance cyclist safety in traffic scenarios.

Contribution

The study introduces a novel cyclist detection model fine-tuned on a large dataset, deployed on a mini-computer, demonstrating real-time performance for safety applications.

Findings

Achieved >0.900 mAP on cyclist detection

Inference times as low as 15 milliseconds

Successful real-time deployment on embedded hardware

Abstract

Cycling is an increasingly popular method of transportation for sustainability and health benefits. However, cyclists face growing risks, especially when encountering large vehicles on the road. This study aims to reduce the number of vehicle-cyclist collisions, which are often caused by poor driver attention to blind spots. To achieve this, we designed a state-of-the-art real-time monocular cyclist detection that can detect cyclists with object detection convolutional neural networks, such as EfficientDet Lite and SSD MobileNetV2. First, our proposed cyclist detection models achieve greater than 0.900 mAP (IoU: 0.5), fine-tuned on a newly proposed cyclist image dataset comprising over 20,000 images. Next, the models were deployed onto a Google Coral Dev Board mini-computer with a camera module and analyzed for speed, reaching inference times as low as 15 milliseconds. Lastly, the end-to-end cyclist detection device was tested in real-time to model traffic scenarios and analyzed further for performance and feasibility. We concluded that this cyclist detection device can accurately and quickly detect cyclists and has the potential to improve cyclist safety significantly. Future studies could determine the feasibility of the proposed device in the vehicle industry and improvements to cyclist safety over time.