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In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

In recent years, with the growth of China's economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people's lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide. [ABSTRACT FROM AUTHOR]

Because of the shortage of rule gap measurement for light guide plate (LGP) warpage degree, image definition criterion of quality indexing is present based on single CCD camera. The detection paper and LGP are respectively arranged on the camera detection device platform, located by X- and Y-axes driving unit and focused by Z-axis driving unit firstly, different image definition criterion peaks of edge detection evaluation function are obtained in the middle surface of LGP bottom and surface. Finally, different edge detection algorithms are compared under different situations. Results show that the algorithm has good repetition and can meet the online, untouched detection needs. [ABSTRACT FROM AUTHOR]

Sub-pixel accuracy stripe boundary location is essential for stripe coded structured light based 3D reconstruction. So far, the intersecting point method might be the most accurate for stripe boundary location. But in this method two groups of patterns have to be projected, where one group is normal and the other is inverse patterns. So the intersecting point method can not be used in several time- or resource-constrained applications because of its complexity of projection. In this paper, after a brief survey on several commonly used stripe boundary detection methods, an improved sub-pixel location scheme based on weighted centroid algorithm is proposed. In the proposed scheme, a green slit is first added between every stripe to avoid false detection and false rejection. Then, universal gravity-based edge detection and weighted centroid method are utilized for sub-pixel location. Compared with the intersecting point method, the proposed location scheme can achieve comparable detection accuracy with only half number of patterns projected. [ABSTRACT FROM AUTHOR]