Counting steel bars is a routine daily task for steel manufacturers and most building construction sites. Currently, counting is usually performed manually, which is laborious, time-consuming, and error-prone. This study proposes a deep learning approach based on YOLOv3 detector for automatic steel bars detection and counting through images. Three new measures, including an additional feature pyramid, complete intersection over union (IoU) loss and focal loss, and bag of freebies, were introduced to improve rebar detection and counting performance. A dataset containing 74,824 rebar sections from real construction sites was constructed and utilized to evaluate the proposed approach. The application results demonstrate that the suggested measures can significantly improve the performance of the YOLOv3 detector to high average precision of 99.7% at IoU of 0.5. Comparisons with other detectors show that the proposed approach is fast, accurate, and robust at rebar counting under different construction site conditions. It can be facilitated as the basis of a real-time, cost-effective rebar counting scheme.

对钢筋制造商和大多数建筑工地来说，盘点钢筋是日常工作。当前，计数通常是手动进行的，这很费力，费时且容易出错。这项研究提出了一种基于YOLOv3检测器的深度学习方法，用于自动钢筋检测和通过图像计数。为了提高钢筋的检测和计数性能，引入了三个新措施，包括附加特征金字塔，完全交集（IoU）丢失和焦点丢失以及免费赠品。构建了包含来自实际建筑工地的74,824个钢筋截面的数据集，并将其用于评估所提出的方法。应用结果表明，所建议的措施可以显着提高YOLOv3检测器的性能，使其平均精度达到99％。当IoU为0.5时为7％。与其他检测器的比较表明，在不同的建筑工地条件下，所提出的方法在钢筋计数方面是快速，准确和可靠的。可以方便地将其作为实时，经济高效的钢筋计数方案的基础。