Underwater object detection in side-scan sonar (SSS) imagery is a challenging task due to limited labeled data and the spatially varying resolution inherent to SSS images. To address these issues, we generate a large-scale synthetic dataset of labeled SSS images using Blender, designed for training and evaluating mine detection models. With a total of 10,000 images, it exceeds existing public datasets by one order of magnitude. The dataset is used to investigate whether applying single image super-resolution (SISR) techniques, such as SPAN and DRCT, to create input images with uniform spatial resolution can enhance detection performance; moreover, SISR can also be used to standardize inputs across data sources, thus reducing the need to retrain detectors for new sensors. Experiments are conducted using the YOLOv11 detector, comparing detection results on both native synthetic images and their super-resolved counterparts. The experimental results show that super-resolution pre-processing permits to achieve detection performance comparable to, and occasionally surpassing, that on high-resolution synthetic data. These findings demonstrate the utility of synthetic datasets in overcoming data scarcity and highlight the potential of SISR in improving underwater object detection in scenarios with spatially varying image quality.