Deep learning-based single-target object segmentation method and detection equipment

The invention belongs to the technical field of image processing in motor snap spring groove height detection, and discloses a deep learning-based single-target object segmentation method and detection equipment, and the method comprises the steps: segmenting a complete target through an improved UN...

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Hauptverfasser: WANG CHANGKAI, LIU ZHICHANG, ZHANG YASHENG, WANG DONGNIAN, XU RUILONG
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creator WANG CHANGKAI
LIU ZHICHANG
ZHANG YASHENG
WANG DONGNIAN
XU RUILONG
description The invention belongs to the technical field of image processing in motor snap spring groove height detection, and discloses a deep learning-based single-target object segmentation method and detection equipment, and the method comprises the steps: segmenting a complete target through an improved UNet neural network under the condition that the same type of incomplete target and complete target exist at the same time, and filtering out the complete target. According to the method, the UNet neural network is improved, when the same type of incomplete targets and complete targets exist at the same time, the network can directly segment the complete targets, and the incomplete targets are filtered out. The method is greatly helpful for only considering the condition of the complete target, reduces the interference of the incomplete target, and reduces the segmentation steps of the complete target. Compared with the original UNet network, the improved UNet network has the advantage that the network segmentation e
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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
title Deep learning-based single-target object segmentation method and detection equipment
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