Accurate rebar recognition is a key step of automatic online rebar counting based on image processing. Current popular rebar recognition methods based on front view image usually lead to missing detection for some special rebar such as obscured rebar, oxidation dark rebar and bent rebar. So a novel rebar recognition method based on multi-view images was proposed in this paper. Firstly, rebar images were captured simultaneously from front view and top view using two cameras. Then, the centers of rebar were located respectively in top and front view image. Finally, in the image fusion step, the top recognition result was used to compensate and validate the front recognition result for reducing and eliminating above missing detection. Experiments showed that this method can efficiently recognize above missed rebar and improve rebar recognition rate.
This paper is using the cocoon filament prices index from the website named the east-sericulture-shift-to-west engineering information system as the research background, seeking for the quantitative analysis and forecast of the cocoon filament price index and making forecast and evaluation for market operation. This paper respectively establishes the improved GM(1,1) model and Grey-Markov model and compares the prediction results of the two models. When using the Grey-Markov model, the data prediction accuracy is higher and it can reflect the volatility of the data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.