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Kechen Song(Associate professor)

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  • Supervisor of Doctorate Candidates  Supervisor of Master's Candidates
  • Name (English):Kechen Song
  • E-Mail:
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Gender:Male
  • Degree:博士
  • Status:Employed
  • Alma Mater:东北大学
  • Teacher College:机械工程与自动化学院

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  • Pavement Distress Detection

Automatic Inspection and Evaluation System for Pavement Distress

Image acquisition system.jpg Overview.png



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We propose a three-stage automatic inspection and evaluation system for pavement distress based on improved deep convolutional neural networks (CNNs). First, the system integrates multi-level context information from the CNN classification model to construct discriminative super-features to determine whether there is distress in the pavement image and the type of the distress, so as to achieve rapid detection of pavement distress. Then, the pavement images with distress are fed into the CNN segmentation model to highlight the distress region with pixel-wise. In the segmentation model, a novel pyramid feature extraction module and a novel guidance attention mechanism are introduced. Finally, we evaluate the degree of pavement damage according to the segmentation results of the CNN segmentation model.  
Hongwen Dong, Kechen Song, et al.  Automatic Inspection and Evaluation System for Pavement Distress [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8),12377-12387.  (paper



Detection and Classification for Pavement Distress Images

Few-Shot Classification

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We propose a new few-shot pavement distress detection method based on metric learning, which can effectively learn new categories from a few labeled samples. We adopt the backend network (ResNet18) to extract multilevel feature information from the base classes and then send the extracted features into the metric module. In the metric module, we introduce the attention mechanism to learn the feature attributes of “what” and “where” and focus the model on the desired characteristics. We also introduce a new metric loss function to maximize the distance between different categories while minimizing the distance between the same categories. In the testing stage, we calculate the cosine similarity between the support set and query set to complete novel category detection. 
Hongwen Dong, Kechen Song, et al.  Deep metric learning-based for multi-target few-shot pavement distress classification [J]. IEEE Transactions on Industrial Informatics,  2022,18(3),1801-1810.   (paper)  (code)  (ESI highly cited, 7/2022-1/2023)HighlyCitedPaper.png


Patch-aware Mutual Reasoning Network (PMRN)
We propose a novel Patch-aware Mutual Reasoning Network (PMRN) that utilizes only the prior knowledge of non-defective samples for defect detection. Concretely, a patch-aware mutual reasoning module and a spatial shuffle perception module are devised to reason mutual dependencies and explore dislocations relationships. Besides, an adaptive soft gated anomaly measurement function is developed to calculate reconstruction deviations, which can soft control the information flow according to the complexity of the current scenario.  PMRN.jpg
Yanyan Wang, Kechen Song, et al.  Unsupervised defect detection with patch-aware mutual reasoning network in image data [J]. Automation in Construction, 2022, 142, 104472.   (paper


RENet


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RENet is proposed for accurate and robust pavement crack detection. The rectangular convolution pyramid module is first built on deep layers so that the features can describe defects with different structures. The optimized contextual information and features of shallower layers are gradually merged into three resolutions. Subsequently, the hierarchical feature fusion refinement module and the boundary refinement module are applied to each branch. These two modules effectively promote the seamless fusion of features at various scales and make the model pay more attention to boundaries. Finally, the outputs of the three branches are integrated to obtain the final prediction map.

Yanyan WangKechen Song, et al. RENet: Rectangular Convolution Pyramid and Edge Enhancement Network for Salient Object Detection of Pavement Cracks [J]. Measurement, 2021, 170, 108698. (paper


Relevance-aware and Cross-reasoning Network (RCN)

This paper proposes a relevance-aware and cross-reasoning network (RCN) for anomaly segmentation of pavement defects, which can segment defects using merely non-defective images for training. A relevance-aware transformer-based encoder is first devised to model intrinsic interdependencies across local features, thus improving representations of complex non-defective images. Next, a dual decoder strategy is proposed to remap the encoder-generated latent dependencies at the local semantic and global detailed levels, respectively. Specifically, a cross-reasoning refinement module is built in the local decoder to reason the crossrelationship between spatial and channel dimensions. Finally, a context-aware abnormal distillation measurement is developed to evaluate the semantic reconstruction deviations during the inference. Under the guidance of semantic affinity, this measurement allows our model to highlight defective areas adaptively. Extensive experimental results on four datasets indicate that RCN outperforms other leading anomaly segmentation methods.

RCN.png

Yanyan Wang, Menghui Niu, Kechen Song, et al. Normal-knowledge-based Pavement Defect Segmentation Using Relevance-aware and Cross-reasoning Mechanisms [J]. IEEE Transactions on Intelligent Transportation Systems, 2022. (paper