YOLOv8-STE: Enhancing Object Detection Performance Under Adverse Weather Conditions with Deep Learning

Object detection powered by deep learning is extensively utilized across diverse sectors, yielding substantial outcomes. However, adverse weather conditions such as rain, snow, and haze interfere with images, leading to a decline in quality and making it extremely challenging for existing methods to detect images captured in such environments. In response to the problem, our research put forth a detection approach grounded in the YOLOv8 model, which we named YOLOv8-STE. Specifically, we introduced a new detection module, ST, on the basis of YOLOv8, which integrates global information step-by-step through window movement while capturing local details. This is particularly important in adverse weather conditions and effectively enhances detection accuracy. Additionally, an EMA mechanism was incorporated into the neck network, which reduced computational burdens through streamlined operations and enriched the original features, making them more hierarchical, thus improving detection stability and generalization. Finally, soft-NMS was used to replace the traditional non-maximum suppression method. Experimental results indicate that our proposed YOLOv8-STE demonstrates excellent performance under adverse weather conditions. Compared to the baseline model YOLOv8, it exhibits superior results on the RTTS dataset, providing a more efficient method for object detection in adverse weather.