Robust Sewer Defect Detection With Text Analysis Based on Deep Learning

Abstract

Sewerage systems play a vital role in building modern cities, providing appropriate ways to release liquid wastes. Due to the rapid expansion of cities, the deterioration of sewage pipes are increasing. Hence, systematic maintenance methods are require to overcome this problem. In most cases, sewer inspection is done by human inspectors, which is error-prone, time-consuming, costly, and lacking appropriate survey evaluations. In this paper, we introduce a new automated framework for detecting sewage pipe defects based on the attention mechanism, improved YOLOv5 architecture, and location information recognition from CCTV videos. The main contributions include (1) the addition of a micro-scale detection feature in the layers to improve the defect detection mechanism; (2) the application of a convolutional block attention module for better channel/spatial features; (3) construction of a larger defect-detection dataset for the 12 most common defect types; and (4) implementation of the TPS-ResNet-BiLSTM-Attn (TRBA) model for the text-information recognition mechanism from CCTV videos. The experimental results show that the proposed real-time sewer defect detection model achieved the mean average precision (mAP) of 75.9% on the proposed dataset, outperforming other standard models, such as YOLO and SSD.

A Sewer Pipeline Defect Detection Method Based on Improved YOLOv5

“…To objectively demonstrate the effectiveness of the improved YOLOv5 model proposed in this paper for detecting defects in sewer pipes, some mainstream object detection models, including SSD, Faster R-CNN, and the YOLO series, were trained and tested on the same dataset. Moreover, the enhancement strategy proposed by Chanmi et al [14] (YOLOv5LC, micro-scale detection layer + CBAM) for the detection of sewer pipeline defects using the YOLOv5s model has been successfully reproduced in this study. Subsequently, a comparative analysis was conducted between the improved model and theirs.…”

“…Tan et al [13] utilized Mosaic data augmentation on top of YOLOv3, introduced generalized intersection over union (GIOU), and employed adaptive anchor boxes. Chanmi et al [14] utilized YOLOv5 as the architecture and incorporated a small object detection layer while introducing attention mechanisms to enhance the detection accuracy of small objects.…”

Data-Driven Analysis and Integrated Modeling of Climate Change Impacts on Coastal Groundwater and Sanitary Sewer Infrastructure

“…issues. By leveraging state-of-the-art computer vision techniques, preliminary studies in the literature [such as Tan et al (2021) and Oh et al (2022)] have provided promising prospects for implementing AI-based models for sewer defect detection from massive CCTV videos.…”