Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting

Abstract

In this study, we propose a novel lightweight detection model for rebar counting, which is rectified mobilenet lightweight feature pyramid network based on YOLO (RM-LFPN-YOLO). The model incorporates a lightweight backbone network that integrates the coordinate attention (CA) mechanism, a lightweight feature pyramid network (LFPN), and a loss function that combines focal loss and efficient intersection over union (EIOU) loss, all meticulously designed to enhance the model’s performance. Experimental results demonstrate that our improved algorithm, with a mere 25.08M parameters, computes efficiently at 7.60G with an input size of 416 pixels. Additionally, it achieves an impressive average precision (AP) of 99.03% at an IOU of 0.5. The proposed lightweight model can be deployed on embedded devices and achieve efficient rebar detection and counting performance.