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 in-put resolutions of 160×160, 320×320, and 640×640 pixels, we conducted experiments on the VisDrone and KITTI datasets, which represent scenarios where small and distant objects are prevalent. Our results indicate that the image center ROI cropping technique improved the detection of small objects, particularly at a 320×320 resolution, achieving enhancements of 6.67× and 1.27× in mean Average Precision (mAP) on the VisDrone and KITTI datasets, respectively. However, excessive cropping negatively impacted the detection of medium and large objects due to the loss of peripheral contextual information and the exclusion of objects outside the cropped region. Image slicing and re-slicing demonstrated impressive improvements in detecting small objects, especially using the grid-based slicing strategy on the VisDrone dataset, with an mAP increase of 2.24× over the baseline. Conversely, on the KITTI dataset, although a performance gain of 1.66× over the baseline was observed for small objects at a 320×320 resolution, image slicing adversely affected the detection of medium and large objects. The fragmentation of objects at image slice borders caused partial visibility, which reduced detection accuracy. These findings contribute to the development of more effective and efficient ARAS technologies, ultimately enhancing the safety of powered two-wheeler riders. Our evaluation code scripts are publicly accessible at: https://github.com/Luna-Scooters/SOD using image preprocessing.