Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder–decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional features produced by the decoder. Convolutional blocks with residuals are used to capture the strong semantic features of cracks, and an adaptive filter fusion module is proposed to assist the network make a selection of filter fusion features on the channels. CrackF-Net fuses the multiscale features in decoder to improve crack detection performance. The proposed CrackF-Net is compared to other advanced crack detection methods using three public datasets. The experimental results show that CrackF-Net achieves state-of-the-art performance, which obtains F-measures of 0.866, 0.737, and 0.852 on the three datasets.
In the realm of picture forensics, it might be difficult to find and locate an image-splicing forgery. To improve the accuracy of the picture forensic evaluation, we introduce a dual encoder network (DAE-Net) with an efficient channel attention (ECA) module. The ECA module creates a fusion approach with an attention mechanism that enables the model to concentrate on local objects’ tampering characteristics and increases the accuracy of multi-region tampering identification. We suggest combining a dual-coding network with a multi-scale dilated convolutional feature fusion module to better detect small target tampering zones. Experimental evidence suggests that DAE-Net outperforms state-of-the-art methods. The attack experiments also demonstrate the DEA-Net model’s stability and noise resistance.
Semantic scene segmentation has become an important application in computer vision and is an essential part of intelligent transportation systems for complete scene understanding of the surrounding environment. Several methods based on convolutional neural networks have emerged, but they have some problems, including small-scale target loss, inaccurate detailed region segmentation, and boundary category confusion. Using shallow features, we exploit the capabilities of global context information according to the theory of pyramids. A weighted pyramid feature fusion module is constructed to fuse the feature maps of different scales generated by the backbone network, and the proportion of feature fusion is dynamically updated by trainable parameters. After that, a self-attention mechanism is introduced to discover information about spatial channel interdependencies. Finally, the atrous spatial pyramid pooling module of the DeepLabv3+ network is improved by connecting the atrous convolution with different dilation rates at the receptive field. The experimental results show 4.1% mean pixel accuracy and 3.92% mean intersection over union improvements in the proposed method compared with the DeepLabv3+, and the result of semantic segmentation is more accurate.
The backlight foreign matters can undermine the display quality because they cause white spots on the thin-film transistor liquid crystal display screen. Research on the failure mechanism for such white spots shows that the issue is caused by the foreign matters between backlight films. Due to the variance in temperature and humidity or the influence of external force, such matters will expand or contract to the extent of off-specification, thus pressing or even scratching the film materials. As a result, the backlight module’s light path will be damaged, and the screen will present abnormal bright spots. To solve this problem, the key factors causing such matters in the process are screened out by fishbone diagram and cause-effect matrix. Then the countermeasures are developed by Six Sigma for the continuous improvement of standardization. Finally, the significance and effectiveness of such countermeasures are verified by double-ratio hypothesis testing and environmental testing. Experimental results show that the failure rate caused by backlight white spots decreases from 0.3% to 0.04% after the improvement. In addition, the fault feedback ratio can reach the standard continuously, indicating significant improvement.
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