DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

Zou, Qin; Zhang, Zheng; Li, Qingquan; Qi, Xianbiao; Wang, Qian; Wang, Song

doi:10.1109/tip.2018.2878966

Cited by 648 publications

(465 citation statements)

References 42 publications

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“…Meanwhile, we notice that deep learning as an emerging technology has demonstrated state-of-the-art, humancompetitive, and sometimes better-than-human performance in solving many computer vision problems such as object detection [10]- [12], image classification/retrieval [13] and semantic segmentation [14]- [17]. There are mainly two types of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

Zou

Jiang

Dai

et al. 2020

IEEE Trans. Veh. Technol.

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303

152

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Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

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Section: Introductionmentioning

confidence: 99%

Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

Zou

Jiang

Dai

et al. 2020

IEEE Trans. Veh. Technol.

Self Cite

303

152

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show abstract

“…In recent years, deep learning has demonstrated stateof-the-art, human-competitive, and sometimes better-thanhuman performance in solving many cognitive problems such as speech recognition [16] and visual perception [17], arXiv:1811.00338v2 [cs.LG] 3 Aug 2019 [18]. There are mainly two types of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Zou

Wang

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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204

141

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Comparing with other biometrics, gait has advantages of being unobtrusive and difficult to conceal. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by average person, which makes it very convenient and inexpensive to collect gait data. In this paper, we study gait recognition using smartphones in the wild. Unlike traditional methods that often require the person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under a condition of unconstraint without knowing when, where, and how the user walks. To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space domain and in the time domain are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones on a total of 118 subjects are used for evaluations. Experiments show that the proposed method achieves over 93.5% and 93.7% accuracy in person identification and authentication, respectively.Index Terms-Gait recognition, inertial sensor, person identification, convolutional neural network, recurrent neural network.

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“…Dung (2019) proposed a method for concrete crack detection using a whole encoder-decoder network with the VGG16-based encoder. Zou et al (2019) proposed a method for detecting cracks in pavement and stone surface images using an encoder-decoder network. Various deep-learning methods have been applied to crack detection in infrastructures, but successful application of these methods for detecting road cracks in black-box images has not been reported.…”

Section: Deep Learning-based Crack Detectionmentioning

confidence: 99%

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Bang

Park

Kim

et al. 2019

Computer aided Civil Eng

345

170

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Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black‐box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road‐image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel‐level detection method for identifying road cracks in black‐box images using a deep convolutional encoder–decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black‐box videos and tested on the remaining 100 images. Compared with VGG‐16, ResNet‐50, ResNet‐101, ResNet‐200 with transfer learning, and ResNet‐152 without transfer learning, ResNet‐152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black‐box images at the pixel level.

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DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

Cited by 648 publications

References 42 publications

Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Encoder–decoder network for pixel‐level road crack detection in black‐box images

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