Comparative analysis of saliency map algorithms in capturing visual priorities for building inspections

This study investigates the efficacy of saliency mapping algorithms in capturing the visual priorities of building inspectors for structural damage assessment. Our work established a ground truth dataset by implementing eye-tracking technology to capture the gaze patterns of building inspectors. Further, it enables a detailed evaluation of the saliency models’ ability to reflect experts' visual attention during inspection tasks. Our comparative analysis assesses the performance of three saliency models— EnDec, DeepGaze, and SALICON— against this ground truth data, using conventional saliency metrics such as Area under the Curve, Similarity, Normalized Scanpath Saliency, Correlation Coefficient, and Kullback-Leibler Divergence. Our findings reveal that while the SALICON model demonstrates a marginally better performance and highlights areas where these models fall short, particularly in accurately reflecting the critical visual properties of inspectors, this insight is crucial for advancing the field. By highlighting these limitations, we have drawn attention to the need for developing more specialized saliency models tailored to the unique demands of building inspection tasks. Thus, the study not only fulfills its objectives of comparative analysis but also contributes to the broader discourse on improving automated structural inspection systems. This study highlights the need to develop specialized computer vision models to address specific building inspection challenges. By identifying strengths and improvement areas, this research contributes valuable insights and highlights the potential and current limitations of applying computer vision techniques to real-world building inspection tasks.