Enhancing Small Object Detection in Resource-Constrained ARAS Using Image Cropping and Slicing Techniques

Chinmaya Kaundanya, Paulo Cesar, Barry Cronin, Andrew Fleury, Mingming Liu, Suzanne Little

Abstract: Powered two-wheelers, such as motorcycles, e-bikes, and e-scooters, exhibit disproportionately high fatality rates in road traffic incidents worldwide. Advanced Rider Assistance Systems (ARAS) have the potential to enhance rider safety by providing real-time hazard alerts. However, implementing effective ARAS on the resource-constrained hardware typical of micromobility vehicles presents significant challenges, particularly in detecting small or distant objects using monocular cameras and lightweight convolutional neural networks (CNNs). This study evaluates two computationally efficient image preprocessing techniques aimed at improving small and distant object detection in ARAS applications: image center region-of-interest (ROI) cropping and image slicing and re-slicing. Utilizing the YOLOv8-nano object detection model at relatively low input resolutions of 160×160, 320×320, and 640×640 pixels, we conducted experiments on the VisDrone and KITTI datasets, which represent scenarios wh

External IDs:dblp:conf/visigrapp/KaundanyaCCFLL25