To address the challenges of target detection in foggy conditions in real-world scenarios, this paper proposes an improved foggy target detection method based on YOLOv8s. The design includes a front-end module, Edge-Dehaze, which employs joint training of dehazing and detection networks and uses the Sobel operator to enhance edge information in foggy images, thereby improving detection performance in foggy environments. The proposed hybrid attention feature fusion module (HAFM) utilizes parallel attention mechanisms to enhance information interaction and fusion between feature maps, increasing the model′s focus on critical features. Additionally, a lightweight shared attention convolutional detection (LSACD) head is designed, which reduces the parameter count of the detection head through shared convolutions and incorporates the SEAM attention mechanism in the shared layer to alleviate occlusion issues in foggy target detection. Experimental results on the RTTS dataset demonstrate that the improved YOLOv8s network achieves a 1.8% increase in mAP50 and a 1.7% increase in mAP50-95 compared to the original YOLOv8s network, with comparable parameter counts, thereby validating the high accuracy and practicality of the proposed method in foggy target detection.