基于深度相机的钢筋骨架尺寸智能检测方法

Intelligent detection method for steel reinforcement skeleton size based on depth camera

钢筋骨架的质量检查在预制钢筋混凝土构件的生产过程中发挥了重要的作用,因为其质量直接影响整个构件的性能和可靠性。目前,对于钢筋骨架的质量检查,主要依赖于传统的人工手段,比如使用钢尺测量和人工计数来评估钢筋的数量和间距等关键指标。然而,这种方法明显存在低效率、高成本、易出错等局限性。为了解决以上问题,本文针对预制钢筋混凝土构件中的多层钢筋骨架,引入深度相机传感器和先进技术,设计了一种新颖的钢筋骨架质量检查方法。首先通过深度相机采集图像,根据深度阈值筛选算法将双层钢筋转化成单层图像;然后利用改进的YOLOv5算法检测出交叉点坐标;最后根据坐标转换,求解绑扎间距。试验结果表明,该方法通过智能化算法提高了检查的准确性,减少了时间消耗,降低了对人工的依赖。

The quality inspection of the reinforced skeleton plays an important role in the production process of prefabricated reinforced concrete components,because its quality directly affects the performance and reliability of the whole component.At present,the quality inspection of rebar skeletons mainly relies on traditional manual means,such as the use of steel ruler measurement and manual counting to evaluate key indicators such as the number and spacing of rebar spacing.However,this approach has significant limitations such as inefficiency,high cost,and error-proneness.In order to solve the above problems,this paper introduces depth camera sensor and advanced technology for the multi-layer reinforcement skeleton in the prefabricated reinforced concrete components,and designs a novel quality inspection method for the reinforcement skeleton,that is,the image is collected by the depth camera,the double-layer reinforcement is converted into a single-layer image according to the depth threshold screening algorithm,and then the intersection coordinates are detected by the improved YOLOv5 algorithm,and finally the binding spacing is solved according to the coordinate conversion.Experimental results show that the proposed method improves the accuracy of inspection,reduces time consumption,and reduces the dependence on manual labor through intelligent algorithms.