Rebar counting detection using object detection based on deep learning

A high-precision DJI Phantom 4 Pro drone was commissioned at five unique construction sites in South Korea during peak productivity hours. Construction supervisors manually controlled the drone, thus ensuring the capture of clearly visible images of the rebars. The drone's path was methodically guided in a vertical trajectory above each column, positioning it directly above the rebars at an estimated altitude of 1 to 2 meters. At each position, still images were captured with the columns nearly centred in each frame. To underscore the pragmatic viability of the proposed method, rebar images were sourced under authentic operational conditions, thus encapsulating the complexities and challenges that typify bustling construction sites. This dataset contained a diverse array of variations in factors such as illumination, scale, and perspective. Moreover, other construction equipment such as scaffolding, timber and moulding were also observed in the images. In total, 728 images of rebars with a resolution of 1,500 × 900 pixels were captured.

本数据集采用高精度大疆精灵4 Pro（DJI Phantom 4 Pro）无人机，于韩国五处各具特色的建筑工地的生产高峰时段完成数据采集作业。由建筑施工安全员手动操控该无人机，以确保清晰捕捉钢筋（rebars）的图像：无人机沿规划好的垂直轨迹在每根立柱上方飞行，将悬停位置精准设于钢筋正上方，预估飞行高度为1至2米；在每个悬停点位均采集静态图像，且立柱在画面中基本处于中心位置。为凸显所提方法的实际应用可行性，本数据集的钢筋图像均采集自真实施工场景，完整涵盖了繁忙建筑工地典型存在的各类复杂情况与挑战。该数据集涵盖了光照、尺度、视角等多种影响因素下的多样化样本，图像中还包含了脚手架、木料、模板等其他施工设备与物料。本次共采集到分辨率为1500×900像素的钢筋图像共计728张。