Quantity Detection of Steel Bars Based on Deep Learning

Abstract

In the actual production environment, the number of steel bars in the construction site is mainly counted manually. For the special task of steel bar detection, a detection and counting method based on depth learning is proposed. The method is applied to the actual production environment instead of the traditional time-consuming and labor-consuming manual counting method. By comparing the traditional detection algorithm with the one-stage and two-stage detection in depth learning. After the algorithm and considering the efficiency of the model, the improved detection algorithm is proposed to adapt to the special task of steel bar detection. In the final evaluation index, the improved one-stage detection algorithm is superior to the improved detection algorithm in the special task of steel bar detection, showing the improvement of performance, and compared with the single-stage detection algorithm. The law has also been improved to a certain extent.

A New Method for Surprise Box Quantity Determination Based on Comparer

“…The data bus makes the system complex and has manufacturing costs, while employing micro-controllers does not have personalized customization requirements. Therefore, the circuit design of the attached device is intricate [10] [11].…”