Papers › Monocular Cyclist Detection with Convolutional Neural Networks

Monocular Cyclist Detection with Convolutional Neural Networks

16 Jan 2023arXiv:2303.11223archive 2025-07-28

Charles Tang

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.

PaperPDF

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

2D Cyclist DetectionObject DetectionTransfer Learningobject-detection

Datasets

Introduced by this paper, per the archive.

CTCyclistDetectionDataset

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
2D Cyclist Detection CTCyclistDetectionDataset EfficientDet Lite 1 mAP IOU@0.5 0.956 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBiFPNCORALConvolutionDepthwise ConvolutionDepthwise Separable ConvolutionEfficientDetInverted Residual BlockNon Maximum SuppressionPointwise ConvolutionReLUSSD

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections