In order to address the issues of blurred features and difficult extraction of road objects in foggy weather, a target detection algorithm based on a dual-channel attention mechanism was proposed. The algorithm detects through feature fusion, and its design approach is as follows. Firstly, GLCA(global & local channel attention) was used to improve the C2f module for backbone and neck feature fusion, enhancing the ability to extract local features of blurred targets. Then, a dual-path structured MPConv (mixed pooling convolution) was designed to improve the downsampling module, increasing the diversity of feature retention and robustness in handling complex scenes. Subsequently, AuxHead(auxiliary detection head) was introduced to enhance the detection effect of distant small targets. Finally, Focaler-CIoU was introduced to optimize the loss function, reducing the weight of easily classified samples and emphasizing severely obstructed samples. Experiments show that on the RTTS dataset, compared to the YOLOv8s baseline model, the GMA-YOLO model achieved a 3.2% increase in mAP@0.5, reaching 78.6%, and a 3.8% increase in recall rate, reaching 71.7%, while reducing the number of parameters. It is concluded that this algorithm effectively improves the accuracy of target detection in foggy scenes.