To address the issue of inadequate target detection performance in the visual perception systems of autonomous vehicles, particularly under complex weather conditions such as fog and rain that introduce environmental noise, we propose a joint optimization target detection algorithm based on adaptive image denoising and multiple attention mechanisms(DMC-YOLO).An image denoising network has been constructed that combines the dark channel prior algorithm with ACE image enhancement technology to improve image quality in challenging weather conditions. Additionally, this network is integrated with the YOLOv8 backbone, utilizing SCDonw convolution to replace standard convolution. By incorporating point convolution and depth convolution, the aim is to reduce computational costs while obtaining richer down-sampling information.The SEAM attention module is employed to merge local and global information within the network. Furthermore, the SA detection head is introduced to emphasize contextual features, allowing for the retention of more detailed information. To enhance the network′s adaptability to various complex environments, linear interval mapping is incorporated into the loss function for reconstructing IoU.Experimental results indicate that, compared to the baseline model, the average accuracy of the improved algorithm increases by 2.9% while reducing the number of parameters by 15%. This effectively enhances the ability of autonomous vehicles to recognize targets in complex environments.The deployment outcomes on EC-R3588SPC and Nvidia Jetson NX edge devices are promising, fulfilling real-time detection requirements even under challenging weather conditions.