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Road crack is one of the prominent problems that can frequently occur in highways and main roads. The manual road crack evaluation is laborious, time-consuming, inaccurate, and it has several implementation issues. Conversely, the computer vision-based solution is very challenging due to the complex ambient conditions, including illumination, shadow, dust, and crack shape. Most of the cracks exist as irregular edge patterns and are the most important features for detection purpose. Recent advances in deep learning adopt a convolutional neural network as the base model to detect and localize crack with a single RGB image. Yet, this approach has an inaccurate boundary for crack localization, resulting in thicker and blurry edges. To overcome this problem, the study proposes a novel and robust road crack detection based on deep learning which also considers the original edge of the image as the additional feature. The main contribution of this work is adapting the original image gradient with the coarse crack detection result and refining it to produce more precise crack boundaries. Extensive experimental results have shown that the proposed method outperforms the former state-of-the-art methods in terms of the detection accuracy.

The era of intelligent transportation systems is evolving, which amends the way to deploy smart vision systems. One prominent challenge is to maintain the quality of highway roads for the safety issues. The manual road crack inspection is laborious, time-consuming, and inaccurate. A robust and automatic crack detection is needed to overcome these issues. Many deep learning based automatic crack detections have been developed. However, the majority of models suffer from inaccurate boundary detection due to the irregular shapes of the edge. Thus, the edge detection plays an important role to detect the gradient of the images. This study focuses on the refinement process, in which the pre-computed crack detection is refined to enhance the detection result with more accurate boundaries. The proposed refinement involves the image gradient as an additional feature. The vision transformer is adopted as the core layer to refine the crack in patch-wise manner. The multi-head attention in the transformer enhances the ability of the refinement network to find the relation of each processed patch. Extensive experiments have shown that the proposed method is able to generate superior crack boundaries than that of the state-of-the-art methods.

Drowsiness and fatigue of the drivers are amongst the significant causes of the car accidents. Every year the number of deaths and fatalities are tremendously increasing due to multifaceted issues and henceforth requires an intelligent processing system for accident avoidance. In relevant with this, an effective driver drowsiness detection system is proposed. The main challenges are robustness of the algorithm towards variation of the human face and real-time processing capability. The first challenge pertaining to the facial variation has been handled well using conventional image processing and hand-craft features of computer vision algorithms. Yet, variations such as facial expression, lighting condition, intra-class variation, and pose variation are additional issues that conventional method failed to address. Deep learning is an alternative solution which provides a better performance by learning features automatically. Thus, this paper proposed a new concept for handling the real-time driver drowsiness detection using the hybrid of convolutional neural network (CNN) and long short-term memory (LSTM). The performance of the system has been tested using the public drowsy driver dataset from ACCV 2016 competition. The results show that it can outperform the former schemes in the literature.

Image segmentation is one of the popular techniques for vision application, i.e., retrieving object information from an image. Segmentation not only provides the class and location of an object, but the associated contour as well. Recent advances of the segmentation deploy the encoder and decoder architectures with skip connection, and also utilize the edge information to obtain the best contour. This work focuses on maintaining the information flow from skip connection, and also identifies suited feature from the bottom layer. The feature selector can be also deployed in edge information for yielding segmentation contour. Designed filters are also employed to analyze the best feature for the reconstruction purpose. Experiment results show that the proposed scheme can improvise the performance of the simple ENet to achieve a superior IoU from 51.3% to 57.9%.

Fingerprint is one of popular biometric identifiers for authenticating users. Its permanence characteristic has made it reliable to use. Therefore, fingerprint data should be protected and not be disclosed to unauthorized parties. Neverthelless, its instability scanning result has made conventional cryptographic algorithms do not work. In this paper, we propose an algorithm for protecting fingerprint data by transforming it before being stored in a database. Once it has been transformed, it is hard to reconstruct its original data. In the authentication process, the fingerprint is also transformed in the same way as its registration. So, the original data have never been revealed. The experimental result, which is done by using a public database, shows that the error rate caused by the transformation is relatively low.