AW-YOLO: a multi-object detection network for autonomous driving under all weather conditions

Abstract Over the years, object detection technology based on deep learning has attracted extensive research in autonomous driving. Achieving a robust object detection network under all weather conditions (e.g., sunny, fog, nighttime, rain, and snow) is highly significant for autonomous driving systems, which ensure safety by recognizing pedestrians, vehicles, traffic lights, etc. This paper proposes a robust multi-object detection network named AW (All Weather)-YOLO based on YOLOv8, with a trade-off between precision and lightweightness. Considering the blurring or absence of the salient object features of the image under all weather conditions, we propose a developed dilation-wise residual (D-DWR) module. Specifically, it combines the dilatation-wise residual module with the dilated re-param block using a large kernel convolution to see wide without going deep, greatly improving the feature extraction ability. Moreover, we introduce an efficient dynamic upsampler (DySample) that formulates upsampling from the viewpoint of point sampling and avoids dynamic convolution, which can improve the network's ability to feature fusion. Lightweight is an essential requirement for autonomous driving. To this end, we adopt a multi-scale shared detection head (MSSD-Head) to achieve lightweight deployment in autonomous vehicle. Experimental results show that the mAP50-95 values of AW-YOLO on the KITTI and ACDC datasets exceed the baseline model YOLOv8 by 1.7% and 1.5%, respectively. Meanwhile, the parameters and model size of AW-YOLO have decreased by 21.4% and 20.4%, respectively.