Monocular depth estimation for screw tightness state detection

Abstract Accurate detection of industrial Screw assembly status is crucial for ensuring product quality and safety. This paper proposes an efficient monocular vision-based method for detecting Screw tightness, significantly reducing reliance on expensive depth sensors. By constructing a dataset encompassing various Screw types and fastening states, the system employs a monocular depth estimation model based on Depth Anything V2 and the Dense Prediction Transformer to generate relative depth maps. To overcome the limitations of relative depth information, this paper introduces a novel normalized feature extraction method that computes depth DIFs between the Screw region and its surrounding area to extract robust Screw-state representation features. Building on this, we design a Sparse Convolutional Residual 4-path Network that integrates a convolutional block attention module to effectively fuse RGB images with normalized depth features. Finally, the fused features and scalar depth information are fed into a regressor to predict Screw-to-surface distances, and the tightness state is determined via threshold comparison. Experimental results demonstrate that this method can accurately identify subtle variations in Screw states, offering a practical, vision-based alternative for automated Screw tightness monitoring in defined industrial scenarios where stable top-down views are maintained.