Production Line Action Behaviour Recognition Based On Dynamic Attention Mechanism in Sequence Space

Product surface integrity inspection is a key step to ensure product quality, and is usually done manually by quality inspectors. In order to reduce the mistakes caused by fatigue caused by repeating a single action for a long time, this paper intends to use an auxiliary surface integrity detection system based on deep learning to remind and check whether it is complete. Because there are few action datasets in industrial scenes, we propose Surface Integrity Check Dataset (SIC), a new large-scale dataset for human behavior understanding in industrial scenarios. The SIC dataset provides a series of action videos of quality inspectors performing product surface integrity inspections under industrial scene conditions, specifically showing whether the quality inspectors perform a complete spatial 360° inspection of the product. The SIC dataset contains 9183 31-category object appearance inspection samples and 2 action categories, and for each video clip, we provide videos from three perspectives. At the same time, an action behavior intelligent detection model MaskX3D based on deep learning technology is proposed. This model uses a large convolution kernel for efficient feature extraction, adaptively removes irrelevant background information in the scene, and conducts experiments on the self-built SIC dataset. The experimental results show that the recognition accuracy of the model proposed in this paper is 93.82% on the self-built dataset, which is better than 91.29%, 91.73% and 93.57% of the existing I3D, SlowFast and X3D networks, meeting the field accuracy and Lightweight requirements.