End Face Detection of Rebar Based on Improved PP YOLO

Abstract

This paper introduces an improved PP-YOLO network method to enhance the accuracy of rebar end identification and counting in construction engineering. The network is optimized for the specific characteristics of rebar images, enhancing its recognition capabilities for rebars of various sizes and shapes. Notably, the network structure introduces new pathways in the 4th and 5th layers, enabling more effective learning and identification of rebar features from low-level characteristics, thus improving overall recognition and counting accuracy. Additionally, an optimized data augmentation strategy, tailored to the unique features of rebar images, replaces the traditional Mixup method. Specific algorithms are introduced to enhance the network's efficiency in learning rebar image characteristics. These improvements led to excellent performance in rebar end face recognition tests, achieving an average precision (AP) of 96.23%, a 1.07% increase compared to the original model. This significant performance improvement confirms the effectiveness of our proposed improvements in practical applications, offering new perspectives for the development of rebar detection technology in the construction industry.