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Keywords = improved ZS thinning

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21 pages, 14494 KiB  
Article
Track Line Recognition Based on Morphological Thinning Algorithm
by Weilong Niu, Zan Chen, Yihui Zhu, Xiaoguang Sun and Xuan Li
Appl. Sci. 2022, 12(22), 11320; https://doi.org/10.3390/app122211320 - 8 Nov 2022
Cited by 3 | Viewed by 1756
Abstract
In the field of intelligent driving of freight trains, determining the track line ahead of the train is an important function in the autopilot technology of such trains. Combining the characteristics of freight railway tracks, we conduct an in-depth analysis of the shortcomings [...] Read more.
In the field of intelligent driving of freight trains, determining the track line ahead of the train is an important function in the autopilot technology of such trains. Combining the characteristics of freight railway tracks, we conduct an in-depth analysis of the shortcomings of object detection technology in extracting track lines and propose an improved Zhang–Suen (ZS) thinning theory for a railway track line recognition algorithm. Through image preprocessing and single pixel thinning steps, a continuous track line is obtained and then processed by a denoising algorithm to obtain a complete track line. Experimental results show that the track extracted by our method has good continuity and less noise. It can simultaneously perform track detection on straight roads, curves and turnouts, and is suitable for changing weather conditions such as sunny daytime, mild rainy daytime, cloudy daytime, night with lamp lighting and night without lamp lighting conditions. Full article
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18 pages, 6245 KiB  
Article
3D Locating System for Pests’ Laser Control Based on Multi-Constraint Stereo Matching
by Yajun Li, Qingchun Feng, Jiewen Lin, Zhengfang Hu, Xiangming Lei and Yang Xiang
Agriculture 2022, 12(6), 766; https://doi.org/10.3390/agriculture12060766 - 27 May 2022
Cited by 6 | Viewed by 2687
Abstract
To achieve pest elimination on leaves with laser power, it is essential to locate the laser strike point on the pest accurately. In this paper, Pieris rapae (L.) (Lepidoptera: Pieridae), similar in color to the host plant, was taken as the object and [...] Read more.
To achieve pest elimination on leaves with laser power, it is essential to locate the laser strike point on the pest accurately. In this paper, Pieris rapae (L.) (Lepidoptera: Pieridae), similar in color to the host plant, was taken as the object and the method for identifying and locating the target point was researched. A binocular camera unit with an optical filter of 850 nm wavelength was designed to capture the pest image. The segmentation of the pests’ pixel area was performed based on Mask R-CNN. The laser strike points were located by extracting the skeleton through an improved ZS thinning algorithm. To obtain the 3D coordinates of the target point precisely, a multi-constrained matching method was adopted on the stereo rectification images and the subpixel target points in the images on the left and right were optimally matched through fitting the optimal parallax value. As the results of the field test showed, the average precision of the ResNet50-based Mask R-CNN was 94.24%. The maximum errors in the X-axis, the Y-axis, and the Z-axis were 0.98, 0.68, and 1.16 mm, respectively, when the working depth ranged between 400 and 600 mm. The research was supposed to provide technical support for robotic pest control in vegetables. Full article
(This article belongs to the Special Issue Robots and Autonomous Machines for Agriculture Production)
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