基於深度學習之路面破損檢測

Abstract

目前國內的道路維護方式多為定期派遣工程車檢測以及依賴人民的通報，而為了盡早的發現道路損壞並進行修復，本研究運用Mask R-CNN深度學習之方式建立道路破損辨識模型。透過Mask R-CNN深度學習演算法，以及運用python 、OpenCV撰寫進行道路破損檢測與資料整合，持續的分析模型數據並根據結果進行再訓練。利用路面破損辨識模型檢測出路面上的龜裂、裂縫、補綻、變形以及坑洞，並在龜裂、裂縫、補綻、變形達到86%以上的召回率，精確率除了裂縫、坑洞之外有82%以上，此外對檢測出來的破損範圍進行面積計算，為日後養護維修提供面積的量化指標，進而輔助人力巡查作業。

At present, domestic road maintenance methods are mostly dispatching construction vehicles for inspection and relying on people’s notifications. In order to discover and repair road crack as soon as possible, this study uses Mask R-CNN deep learning to establish a road crack identification model. Through the Mask R-CNN deep learning algorithm, and the use of python and OpenCV to write for road crack detection and data integration, continuous analysis of model data and retraining based on the results. The road surface crack identification model is used to detect alligator crack, linear crack, patch, deformation and pothole on the road, and the recall rate of alligator crack, linear crack, patch and deformation is over 86%. Except for linear crack and pothole, the accuracy rate is more than 82%. In addition, the area of the detected damage area is calculated to provide a quantitative indicator of the area for future maintenance and repair, and then assist manpower inspection operations.