Lightweight Rebar End Detection Algorithm Based on Improved YOLOv8

Abstract

Purposes Rebar plays an indispensable role in construction engineering; however, challenges such as densely packed end faces, non-uniform diameter scales, adhesive boundaries, background fusion, and occlusions between end faces have made precise counting a significant challenge. In recent years, deep learning has made remarkable strides in the field of dense object counting. Nonetheless, deep leaming faces limitations because of the need for large-scale data and computational resources, hindering its practical application. Methods In response to these challenges, an enhanced YOLOv8 model framework is introduced for rebar end detection. The framework incorporates Spatial and Channel reconstruction Convolutional (SCConv) modules and the Normalized Wasserstein Distance (NWD) loss function tailored for small object detection. Findings Experimental results from ablation tests demonstrate that the SCConv module significantly reduces network parameters while maintains network performance. Furthermore, the NWD loss function notably enhances the accuracy of rebar end detection in large models. This re search provides an effective solution for achieving high-precision and lightweight rebar counting.

Keywords

• deep learning
• yolov8
• rebar counting
• detection method