This paper applies a two-stage fine-tuning method to recognize and detect the openings of bulk cement tank trucks, aiming to explore the effectiveness of few-shot object detection models in specific application scenarios. We first introduce the application of object detection technology in industrial automation, and then created a custom few shot dataset for the opening of bulk cement truck openings, and a detailed exploration of object detection algorithms based on few-shot learning. In the experimental methods section, we describe the process of dataset construction and the specific settings of the experiments. Finally, we summarized the experimental results, confirming the effectiveness and scalability of the adopted object detection method in few-shot and specific application scenarios. We also pointed out some challenges in few-shot learning, such as limited sample size and scene adaptability, and propose future research directions, including exploring more advanced data augmentation techniques and improving object detection accuracy in different scenarios.