Evolution and future trends of rebar intersection detection methods

Reinforcement intersections are critical for automated tying, directly affecting positioning accuracy and construction efficiency. This paper reviews detection methods ranging from traditional image processing and geometric features to deep learning models and hybrid approaches. It discusses the advantages and limitations of each method: traditional methods are effective in simple scenarios, deep learning enhances adaptability and accuracy, and hybrid strategies balance robustness with efficiency. However, none of these represent the optimal solution. Therefore, this study proposes a potential development framework to inspire future research.