There are limited memory and computing power of the equipment in smart construction sites， making it very difficult to detect rebar in real time through object detection on the on-site equipment. The slow speed of rebar detection and the high cost of model deployment of this equipment also bring great challenges. In order to solve the problems， RebarNet， a lightweight network for rebar detection with attention mechanism was proposed on the basis of YOLOv3 （You Only Look Once version 3）. Firstly， the residual block was used as the basic unit of the network to construct a feature extraction structure to extract local and contextual information. Secondly， Channel Attention （CA） module and Spatial Attention （SA） module were added to the residual block to adjust the attention weight of the feature map and improve the ability of the network to extract features. Thirdly， the feature pyramid fusion module was used to increase the receptive field of the network and optimize the extraction effect of the medium-sized rebar images. Finally， the feature map of 52×52 channel was output for post-processing and rebar detection after 8 times downsampling. Experimental results show that the parameter amount of the proposed network is only 5% of that of Darknet53 network， and mAP （mean Average Precision） of the proposed network achieves 92.7% at the speed of 106.8 FPS （Frames Per Second） on the rebar test dataset. Compared with the existing 8 object detection networks including EfficientDet （Scalable and Efficient Object Detection）， SSD （Single Shot MultiBox Detector）， CenterNet， RetinaNet， Faster RCNN （Faster Region-CNN）， YOLOv3， YOLOv4 and YOLOv5m （YOLOv5 medium）， RebarNet has a shorter training time （24.5 seconds）， the lowest memory usage （1 956 MB）， and the smallest model weight file （13 MB）. Compared with the current best-performing YOLOv5m network， RebarNet has the mAP slightly lower by 0.4 percentage points with the detection speed increased by 48 FPS， which is 1.8 times of that of YOLOv5m network. The above indicates that the proposed network helps to complete the task of high-efficiency and accurate rebar detection in smart construction sites.