Surface defect detection is a critical task in industrial production process. Nowadays, there are lots of detection methods based on computer vision and have been successfully applied in industry, they also achieved good results. However, achieving full automation of surface defect detection remains a challenge, due to the complexity of surface defect, in intra-class, while the defects between inter-class contain similar parts, there are large differences in appearance of the defects. To address these issues, this paper proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multi-scale features are extracted at first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of predict. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean Intersection of Union and mean Pixel Accuracy

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Rain and snow bring poor visibility at outdoor vision systems. The common used image processing methods may be not suitable for a degraded image. In this paper, a guidance image method is proposed to remove rain and snow in a single image. To removal rain and snow only using one image, a guidance image is derived from the imaging model of a raindrop or a snowflake when it is passing through an element on the CCD of the camera. Since only using this guidance image may lose some detailed information, in this paper, a refined guidance image is proposed. This refined guidance image has similar contour with the un-degraded image and also maintains the detailed information which may be lost at the guidance image. Then a removal procedure is given by the use of the refined guidance image. Some comparison results are made between different methods using the guidance image and the refined guidance image. The refined guidance image can be used to get a better removal result. Our results show that this proposed method has both good performance in rain removal and snow removal.

The raindrops degrade the performance of outdoor vision system, and it brings difficulties for objects detection and analysis in image sequence. In this paper, we propose an algorithm detect moving objects using chromatic based properties in rain-affected vi...

Node localization is an important supporting technology in wireless sensor networks (WSNs). Traditional maximum likelihood estimation based localization methods (MLE) assume that measurement errors are independent of the distance between the anchor node and a target node. However, such an assumption may not reflect the physical characteristics of existing measurement techniques, such as the widely used received signal strength indicator. To address this issue, we propose a distance-based MLE that considers measurement errors that depend on distance values in this paper. The proposed distance-based MLE is formulated as a complicated nonlinear optimization problem. An exact solution is developed based on first-order optimal condition to improve the efficiency of search. In addition, a two-dimensional search method is also presented. Simulation experiments are performed to demonstrate the effectiveness of this localization. The simulation results show that the distance-based localization method has better localization accuracy compared to other range-based localization methods.

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