2023/01/16 by Charles L. Tang, Tang, Charles
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4.0 #IoT and GPS-based Vehicle Safety Systems #J.7 #Robotics (cs.RO) #Traffic Prediction and Management Techniques #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.2303.11223
openalex publication_date 2023/01/16 · openalex created_date 2023/03/23 · openalex updated_date 2026/07/28
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.