In the fields of intelligent transportation systems and security monitoring, the accuracy of target detection technology is of great significance. However, in addition to the normal traffic environment, adverse weather conditions such as rain and snow severely restrict the accuracy of target detection. Rain and snow weather make images blurry, greatly increasing the difficulty of feature extraction for targets such as pedestrians and vehicles, resulting in large errors in detection results and affecting the effective operation of related systems. To address this challenging problem, this paper proposes an optimized target detection method for special weather conditions like rain and snow based on the YOLOv7 algorithm. Firstly, a widely-used dark channel de-fogging algorithm and a rain and snow removal algorithm based on guided filtering are introduced to preprocess images affected by rain, snow, and fog. This effectively eliminates the image degradation caused by weather factors and restores clear details of the images. Secondly, the Deep Image Prior (DIP) module is combined with the convolutional neural network-post-processing (CNN-PP) module. Through weakly supervised learning, the method further excavates the target features in the images, enhancing the algorithm’s recognition ability for targets in complex weather conditions. Extensive experimental results demonstrate that the improved algorithm performs excellently in terms of detection accuracy. Compared with the YOLOv5 algorithm, its detection accuracy has increased by 23.7%, and a significant growth of 11.9% has been achieved compared with the original YOLOv7 algorithm. These results fully prove the effectiveness and superiority of the proposed method in target detection scenarios under special weather conditions. It provides reliable technical support for the stable operation of intelligent transportation, security monitoring, and other fields in adverse weather, showing important practical application value and broad development prospects.