A Visual Attention Analysis Method for Industrial Patrol Inspection Based on Eye Tracking and Deep Learning

Manual industrial patrol inspection relies heavily on on-site visual observation, while the visual attention of inspectors toward specific target equipment is difficult to quantify. This study proposes an industrial patrol inspection visual attention analysis method integrating eye tracking and deep learning-based object detection. A wearable eye-tracking device is used to acquire first-person inspection videos, eye-tracking data, and camera parameters. After temporal alignment of the multimodal data, the 3D gaze points are projected onto the corresponding 2D inspection video frames. YOLO12m is employed to detect target equipment, followed by frame-by-frame matching between the 2D gaze points and the equipment bounding boxes. Based on the matching results, seven visual attention metrics are calculated to quantitatively characterize visual attention during patrol inspection. On-site experiments with 10 participants yielded 21 inspection records, with overall visual attention scores ranging from 43.16 to 89.71 and averaging 75.99. Three experts independently rated the 21 records, and the system-generated ratings agreed with the majority expert ratings for 19 records. These results demonstrate that, under the present experimental conditions, the proposed method can quantify the visual attention of inspectors toward specific target equipment during industrial patrol inspection using multiple metrics and provide interpretable results.