Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to the identification of the stored-grain pests. We apply for the first time an object detection model to identify rice weevils and maize weevils, which have always been a challenge in the field of the research of stored-grain pests because of their very similar appearance. To conduct our initial study, we created a pre-training dataset of 4000 images and a object detection dataset of 1600 images. In the experiments, we used Faster R-CNN and R-FCN as object detectors and used VGG16, ResNet101 and Inception-ResNet-v2 as feature extractors. In detail, we pre-trained the object detection models on our pre-training dataset, and fine tuned with our object detection dataset. Finally, we demonstrate that the final object detection model outperforms our baseline and shows a nice detection effect with a high accuracy. It is worth noting that our research will have a revelatory influence on stored-grain pest control and grain storage.