Crack detection on concrete surfaces using the YOLOv8 quantization model

Abstract

The condition of concrete structures’ surfaces, most importantly the condition of cracks on the surface and their development over time, is an important and common criterion used to diagnose health conditions and determine their service life. Rapid collection, identification, and monitoring of concrete structures’ surfaces to assess the condition of bridge structures requires a fast-acting system that can meet the actual speed of the inspection process. This article introduces a YOLOv8 quantization model for rapid crack detection in concrete structures, leveraging GPU acceleration suitable for real-time video analysis during bridge inspections. The method incorporates histogram equalization and image enhancement to mitigate lighting issues and improve crack visibility. INT8 quantization reduces model size and accelerates processing while maintaining accuracy through dataset calibration. Converted to TensorRT and integrated into the inference pipeline for optimized GPU and memory management, the YOLOv8 quantization model achieves at least 30 FPS using full HD video footage. Field tests with NVIDIA GPUs demonstrated a 5x reduction in processing time, a 5x FPS increase, and a 6x improvement in GPU utilization, all while maintaining similar RAM usage. The quantum YOLOv8 model is optimized for NVIDIA GPUs, achieving a balance between accuracy and processing speed, allowing workers to analyze full HD videos in real-time at a rate of at least 30 FPS during field inspections