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With the deepening of the fine management, the traditional storage maintenance management model is also facing great challenges. The original artificial maintenance management model has been unable to meet the needs of fine and intelligent storage management. Therefore, in order to explore the research and application of insect pest monitoring in the storage of raw tobacco, a detection network, based on the deep learning technology and target detection technology, was designed for the detection of tobacco meal borer and tobacco beetle. The detection network is designed with lightweight structure. And in order to achieve fast and accurate detection, the main network is built based on Transformer mechanism. The experimental results show that the network can realize real-time detection with the detection speed of 50FPS and the detection accuracy of 96.7% in the GPUs. The insect pest situation in the raw tobacco storage link can be rapidly evaluated by the real-time detection data which combined with the pest detection system. It’s also be realized that the information and intelligence of pest monitoring management in the raw tobacco storage by the way. Then it provides a feasible reference scheme for monitoring and management of pest situation in storage of threshing and rebaking industry.