Intelligent inventory counting method for construction materials based on object detection

Abstract

Enhancing the automation level of inventory checks for construction materials is of great significance for improving construction efficiency and project quality. An automatic rebar counting method was proposed based on the improved YOLOv5 algorithm using digital image processing technology. The YOLOv5 network structure is improved by adding a small-object detection layer to obtain larger feature maps, while a weighted bidirectional feature pyramid is used to fuse multi-scale feature maps, retaining high-level semantic information. Compared to the original YOLOv5x model, the number of parameters is reduced, and the model's robustness and inference speed are enhanced, enabling better detection of small rebars at the edges or those partially occluded. Additionally, to address the issue of limited rebar datasets and the uneven distribution of large-scale and small-scale samples, data augmentation method such as geometric image enhancement, noise addition, and brightness adjustment are used to expand the dataset. In the experiments, the YOLOv5 model with the integrated small-object detection layer and weighted bidirectional feature pyramid is compared with the original model on a test set reflecting actual working conditions. The experimental result show that the improved model achieves better detection accuracy and inference speed, with an average precision of 97.00%, an improvement of 1.30% over YOLOv5x, and a frame rate of 59.79 frames per second, which is 6.88% improvement over YOLOv5x. Meanwhile, the F1-score reaches 96.00%, indicating that the model meets the practical requirements and is ready for deployment in engineering projects. The research findings provide an intelligent technical approach for rebar counting and management in construction projects.

Keywords

engineering construction materials / rebar recognition / small-object detection / YOLOv5