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Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site.
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Towards Interpretable Object Detection by Unfolding Latent Structures
This paper first proposes a method of formulating model interpretability in visual understanding tasks based on the idea of unfolding latent structures. It then presents a case study in object detection using popular two-stage region-based convolutional neural network (i.e., R-CNN) detection systems. The proposed method focuses on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations. It utilizes a top-down hierarchical and compositional grammar model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold the space of latent part configurations of regions of interest (RoIs). It presents an AOGParsing operator that seamlessly integrates with the RoIPooling /RoIAlign operator widely used in R-CNN and is trained end-to-end. In object detection, a bounding box is interpreted by the best parse tree derived from the AOG on-the-fly, which is treated as the qualitatively extractive rationale generated for interpreting detection. In experiments, Faster R-CNN is used to test the proposed method on the PASCAL VOC 2007 and the COCO 2017 object detection datasets. The experimental results show that the proposed method can compute promising latent structures without hurting the performance.
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- Award ID(s):
- 1909644
- PAR ID:
- 10122811
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Vision
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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