This study proposes a two-stage progressive image deraining algorithm for vehicle detection in rainy days. The proposed algorithm aims to improve the accuracy of vehicle detection in rainy days and solve the problem of accuracy degradation caused by rain streak interference in the vehicle detection system of intelligent and connected vehicles. For the algorithm, a two-stage progressive deraining network was developed with light feature extraction and weighting block along with efficient feature transfer and fuse block as the core to realize the mining and capture of rain streak information and achieve accurate deraining. The deraining images were input to benchmark vehicle detector YOLOv5 for verifying the effectiveness of the proposed algorithm. Furthermore, a mix vehicle dataset was constructed based on the working environment of intelligent and connected vehicles. The gains of the proposed deraining algorithm on the precision, recall, and mAP@0.5 of the benchmark vehicle detector YOLOv5 are 3.0 percentage points, 8.9 percentage points, and 7.6 percentage points, respectively, under a rainy traffic scenario compared with other algorithms. The results prove that the proposed deraining algorithm considerably improves the accuracy of vehicle detection in rainy days and can be used in practice.