Fengying Ma
ABSTRACT
Given the current situation that the domestic loading machine system needs to adjust the truck body to stop at the specified position with the specified attitude before automatic loading, to solve the problem that the parking position or body attitude of the truck is not consistent with the posture during teaching because the position or attitude of the truck cannot be adjusted to the specified posture, the automatic loading system cannot complete the automatic loading. In this paper, the positioning and dimension measurement of the truck compartment in the process of automatic loading system are studied. Firstly, two industrial cameras are used to collect the images of the truck from two different fields of vision, and the images are stitched together to obtain the complete images of the truck and vehicle. Then FCN-ResNet50 network with the CBAM attention mechanism is used to segment the truck image. This method can accurately detect the position of the truck in different postures. Secondly, the reference point is selected through the feature plate to determine the length of unit pixel of the image. Finally, the segmentation image of the compartment area is used to locate the compartment area and four corners of the compartment. Combining the length of the unit pixel point and the image coordinate position of the four corner points, the size of the compartment is calculated through the coordinate mapping between the image coordinate system and the world coordinate system. The system is verified by truck model experiment, evaluated by MIoU value and measured truck size results. The experimental results show that the MIoU value of the segmentation image of the carriage can reach 97.5%, and the measured size of the carriage area is within the allowable error range of the actual size of the truck model, which eliminates the restriction that the truck needs to stop in strict accordance with the designated position.
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Index Terms
- Research on Positioning and dimension measurement of truck compartment based on Semantic segmentation in automatic loading system
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