Impact of Input Image Resolution on Deep Learning Performance for Side-Scan Sonar Classification: An Accuracy–Efficiency Analysis

Side-scan sonar (SSS) image classification is crucial for underwater applications, but the trade-off between the accuracy afforded by high-resolution images and the associated computational cost challenges deployment, particularly on resource-constrained platforms like AUVs. This study systematically investigates and quantifies this accuracy–efficiency trade-off in SSS image classification by varying input resolution. Using two distinct SSS datasets and a resolution-adaptive deep learning strategy employing MobileNetV2 and ResNet variants across six resolutions, we evaluated classification accuracy and computational metrics. Results demonstrate a clear inverse relationship: decreasing resolution significantly reduces computational load and processing times but lowers classification accuracy, with the degradation being more pronounced for the more complex four-class dataset. Notably, model test accuracy did not necessarily increase monotonically with resolution. Importantly, acceptable accuracy levels above 90% or 80% could be maintained at significantly lower resolutions, offering substantial efficiency gains. In conclusion, strategically reducing SSS image resolution based on application-specific accuracy requirements is a viable approach for optimizing computational resources. This work provides a quantitative framework for navigating this trade-off and underscores the need for developing SSS-specific architectures for future advancements.