Abstract
Crack detection is a crucial process to ensure the safety and longevity of buildings and other infrastructure. In this paper, we developed a low-cost, automated crack detection robot that leverages CNN, EfficientNet-B0, and YOLOv8 for efficient identification of cracks in concrete surfaces with a curated crack image dataset introduced to support training and evaluation. YOLOv8's real-time object detection enhances crack localization, while CNN and EfficientNet-B0 provide binary classification, ensuring high precision and recall. The system consists of two stages. In the first stage, YOLOv8 detects and localizes wall regions from the video frame, and the bounding boxes are cropped. The second stage performs crack detection using one of three models by analyzing the cropped regions. Cost-effective approaches are also taken for robot design. The robot features a fan-based negative pressure adhesion system, a 4-wheeled skid-steering drive, and an ESP32-CAM for real-time image capture. Its lightweight 3D-printed chassis ensures stability, allowing it to navigate both walls and ceilings while capturing images for crack analysis. Unlike conventional wall-climbing robot designs, this robot incorporates a funnel-shaped body that enhances negative pressure generation and achieves a 44% reduction in duty cycle, significantly lowering power consumption. By combining low-cost hardware with a deep learning pipeline, our system provides a scalable, efficient, and accessible solution for real-time infrastructure inspection at an approximate total cost of $25, with a lightweight web application enabling smartphone-based control. This affordability makes the system more suitable for the developing world, where infrastructure inspection is often limited by budget constraints, labor intensity, and safety risks.
Abstract (translated)
URL
https://arxiv.org/abs/2609.26130
