Object detection and tracking are essential for reliable decision-making in modern applications such as self-driving cars, drones, and industry. Adverse weather can hinder object detectability and pose a threat to the reliability of these systems. As a result, there is an increasing need for efficient image denoising and restoration techniques. In this study, we investigate the use of image purification as a means of defending against weather corruptions. Specifically, we focus on the effect of snow on an object detector and the benefits of efficient desnowification. We find that the performance of a strong image purifying baseline (PreNet) is not constant across different levels of snow intensity, leading to a reduced overall performance in diverse situations. Through extensive experimentation, we demonstrate that adding a lightweight snow detector significantly improves the overall object detection performance without needing to modify the purification model. Our proposed weather-robust architecture exhibits a 40% performance improvement compared to a strong image purification baseline on the gas cylinder counting task. In addition, it leads to significant reductions of the computational power required to run the purification pipeline with a minimal added cost.