Object detection has been applied in many areas, including security, surveillance, automated vehicle systems, and so on. In recent years, the deep learning-based approaches for solving objection detection have achieved great success. However, the performance deterioration under low-light conditions, which makes the existing algorithms trained on JPG images prone to fail, is inevitable in real-world applications (e.g., adverse weather conditions). To this end, we propose the first end-to-end trainable object detection model on RAW images. Inspired by the Image Signal Processing (ISP) pipeline, we design one novel component called \emph{ConvISP}, which aims at predicting ISP parameters. Extensive experimental results demonstrate that the proposed framework works well under low light conditions in different degrees.