In harsh environments such as rain, snow, fog, and night, the road conditions are complex, and the existing road target detection algorithms are less studied, the amount of data is scarce, and the detection accuracy is not high. Aiming at the problems of less road target detection data and more interference factors in harsh environments, a road target detection method based on improved you only look once version 8n (YOLOv8n) in harsh environments is proposed. Based on the Berkeley DeepDrive-IW (BDD-IW) dataset, the improved cutout algorithm is used to simulate the occlusion of the targets to be detected and the uneven visibility of the detected images caused by the harsh environments in real scenarios to enhance the robustness. In YOLOv8n, a space-to-depth convolution (SPD-Conv) module consisting of a space-to-depth layer and a non-strided convolutional layer is added to the previous layer of each cross-stage partial Darknet53 to two-stage feature pyramid network (C2f) module of the backbone section to compensate for the loss of fine-grained image information caused by a strided convolution. On this basis, the C2f module and the parallel polarized self-attention (PPSA) mechanism are used to construct the feature extraction module cross-stage partial DarkNet53 and polarimetric self-attention to two-stage feature pyramid network and replace the C2f module in the backbone of YOLOv8n to reduce the information loss caused by more noise in harsh environments. To further improve the precision, the up-sampling part of YOLOv8n is modified to the content-aware reassembly of the feature operator to effectively aggregate the context information and expand the receptive field. The experimental results show that for BDD-IW data processed with improved cutout, compared with the original network, the improved YOLOv8n network has a small increase in the number of parameters and increases the precision by 6.0% and the mAP50 value by 5.0%, which effectively improves the performance of target detection in harsh environments.