Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network

Chen, Yuhan; Zhong, Shangping; Chen, Kaizhi; Chen, Shoulong; Zheng, Shuguo

doi:10.1145/3372806.3372816

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…For example, a faster region-based convolutional neural network (Faster R-CNN) was applied to identify the specific type and gain the exact location, and it achieved a mean average precision (mAP) of 83% for four classes by adjusting different impact factors (Cheng & Wang, 2018). Chen et al put forward a cost-sensitive defect detection network that can minimize misclassification costs during the learning process (Chen et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

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Attention‐guided multiscale neural network for defect detection in sewer pipelines

Wang

Dang

et al. 2023

Computer aided Civil Eng

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Sanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed‐circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state‐of‐the‐art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections.

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Section: Related Workmentioning

confidence: 99%

“…Chen et al. put forward a cost‐sensitive defect detection network that can minimize misclassification costs during the learning process (Chen et al., 2019). More recently, Yin et al.…”

Section: Related Workmentioning

confidence: 99%