ADWNet: An improved detector based on YOLOv8 for application in adverse weather for autonomous driving

Drawing inspiration from the state‐of‐the‐art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi‐scale attention (EMA) module has been integrated into the backbone. To address the problem of information loss in fused features, Neck has been replaced with RepGDNeck. Simultaneously, to expedite the model's convergence, the bounding box's loss function has been optimized to SIoU loss. To elucidate the advantages of ADWNet in the context of adverse weather conditions, ablation studies and comparative experiments were conducted. The results indicate that although the model's parameter count increased by 18.4%, the accuracy for detecting rain, snow, and fog in adverse weather conditions improved by 22%, while the FLOPs (floating point operations) decreased by 5%. The results of the comparison experiments conducted on the WEDGE dataset show that ADWNet outperforms other object detection models in adverse weather in terms of accuracy, model parameters and FLOPs. To validate ADWNet's real‐world efficacy, data was extracted from a car recorder under adverse conditions on highways, visual inference was conducted, and its accuracy was demonstrated in interpreting real‐world scenarios. The config files are available at https://github.com/Xinyun‐Feng/ADWNet. The authors propose a new detector, ADWNet, specialized for adverse weather detection through a series of improvements to the original detector of YOLOv8. Complete ablation experiments are performed on the series of improvements, and the model is also compared with existing more advanced detections, and the model achieves optimal recognition accuracy, while the FLOPs and parameters are maintained at a low level.