Research on Image Data Mining and Automated Annotation Methods for Safety Monitoring in the Petroleum Industry Based on Agent Collaboration

In the process of promoting the application of the Kunlun Large Model in the field of safety monitoring in the petroleum industry, two major technical bottlenecks have long been encountered: insufficient utilization of massive inspection image data, which makes it difficult to effectively support intelligent analysis, and the persistently high cost of manual annotation, which severely restricts the iterative optimization of algorithm models. This study innovatively proposes a multimodal data mining system based on agent collaboration. By constructing a collaborative framework between a multimodal large model and a lightweight object detection model, the system first utilizes multimodal semantic understanding technology to intelligently pre-screen massive monitoring images. Subsequently, the fine-tuned and optimized lightweight detection model is employed to achieve precise localization and fine-grained recognition of high-risk targets, while simultaneously generating structured annotation data. The research team independently developed an automated data processing pipeline, integrating three core modules: image cleaning, intelligent annotation, and format standardization, ultimately constructing the petroleum industry’s first dedicated industrial safety inspection dataset. This technical solution leverages the complementary advantages of the large model’s semantic understanding and the small model’s detection accuracy, not only significantly improving the semantic parsing accuracy of complex working conditions such as smoke diffusion and early-stage fire incidents, reducing the degree of manual intervention, but also providing a high-quality data foundation for the visual training of the Kunlun Large Model. This effectively promotes the engineering application of agent technology in safety production scenarios such as pipeline inspection in oil and gas fields and production site monitoring.