Despite the high accuracy achieved by current pedestrian detection technologies, their performance is significantly hindered in foggy weather conditions due to image color distortion and an increased false alarm. This often leads to false positives, false negative, and other related issues. To address these limitations, we propose an enhanced FDW-YOLOv8 (Feature-Enhancing-Attention Dark Channel Prior Wise-IOU YOLOv8) model based on the YOLOv8 algorithm. This model incorporates FEAttention(Feature-Enhancing-Attention) mechanism into the original YOLOv8 network to improve the discriminability and robustness of extracted features, thus reducing the miss rate(MR). Furthermore, we employ the WiseIOU(WIoU) loss function to expedite model convergence and optimize the accuracy of anchor frames, effectively handling challenges posed by occlusions and scale variations in foggy environments. Prior to model prediction, we utilize the Dark Channel Prior(DCP) defogging algorithm to preprocess input images, effectively extracting pedestrian information while suppressing irrelevant details, thus minimizing the false positive rate (FPR). To evaluate the performance of our model, we create a specialized Foggy-Pedestrian dataset, focusing on pedestrian detection in foggy conditions. Our experiments demonstrate that the proposed FDW-YOLOv8 model significantly outperforms the benchmark model in terms of reducing FPR and MR, exhibiting high generalization and practical applicability for real-world applications in foggy environments.