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12 November 2024 Research on improving DeepLabv3+ lightweight network for detection of train tarpaulin damage
Bo Cao, Changtao Wang
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133951U (2024) https://doi.org/10.1117/12.3049136
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
In the operation of train pick-up and delivery, there are many problems with human inspection, resulting in potential safety hazards. Considering the wide variety of railway trains, the appearance of the train tarpaulin is selected as the research object, and the damage of the tarpaulin is automatically detected by combining object detection and image segmentation techniques. When using object detection to detect damage, the edge damage detection effect is not ideal, so the image segmentation algorithm is combined with the object detection algorithm, while the traditional segmentation algorithm is not ideal for small damage and narrow and long damage segmentation effects, resulting in low detection accuracy. Therefore, a segmentation algorithm DMV3 is designed, and the DMV3 algorithm is feasible through experiments, which solves the problems of inobvious segmentation and inaccurate detection. Compared with other lightweight segmentation algorithms, it also has great advantages.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Bo Cao and Changtao Wang "Research on improving DeepLabv3+ lightweight network for detection of train tarpaulin damage", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133951U (12 November 2024); https://doi.org/10.1117/12.3049136
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KEYWORDS
Image segmentation
Education and training
Image processing algorithms and systems
Damage detection
Detection and tracking algorithms
Object detection
Image processing
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