Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376602808> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W4376602808 endingPage "49288" @default.
- W4376602808 startingPage "49273" @default.
- W4376602808 abstract "It is difficult to extract small and dense objects with random state, such as grain and impurity, in image of vehicle-mounted dynamic rice grain flow on combine harvester. Therefore, this paper improves Deeplabv3+ by constructing MobileNetv2 in coding layer and adding ECA(Efficient Channel Attention) to Encoder and Decoder to improve extraction accuracy of high-dimensional features in images with a large number of objects with random state. In addition, the YOLOv4 is improved by using Mixup in preprocessing, constructing Mish in Neck and Head, adding ECA to Neck and Prediction of BackBone to improve training precision of small and dense objects and reducing effect of gradient disappearance. And the impurity/breakage rates are assessed based on relationship model between pixel area and quality, improved Deeplabv3+ and YOLOv4. The proposed method was verified by experiments with images acquired on intelligent combine harvester. Compared with existing Deeplabv3+, YOLOv4, U-NET, BP, the extraction accuracy by improved method increased by more than 4.01%. The average relative error and time of impurity/breakage assessment by proposed method were 7.69% and 1.56s. The proposed method can accurately and rapidly assess impurity/breakage rates for dynamic rice grain flow on combine harvester, and further realize closed-loop control of intelligent harvesting operation." @default.
- W4376602808 created "2023-05-17" @default.
- W4376602808 creator A5002831074 @default.
- W4376602808 creator A5004039190 @default.
- W4376602808 creator A5011312647 @default.
- W4376602808 creator A5042202494 @default.
- W4376602808 creator A5082398400 @default.
- W4376602808 creator A5091945978 @default.
- W4376602808 date "2023-01-01" @default.
- W4376602808 modified "2023-09-25" @default.
- W4376602808 title "Impurity/Breakage Assessment of Vehicle-Mounted Dynamic Rice Grain Flow on Combine Harvester Based on Improved Deeplabv3+ and YOLOv4" @default.
- W4376602808 cites W1990493984 @default.
- W4376602808 cites W2063697177 @default.
- W4376602808 cites W2726355056 @default.
- W4376602808 cites W2892075630 @default.
- W4376602808 cites W2902684796 @default.
- W4376602808 cites W2939474476 @default.
- W4376602808 cites W2963163009 @default.
- W4376602808 cites W2979919286 @default.
- W4376602808 cites W3031712369 @default.
- W4376602808 cites W3032082096 @default.
- W4376602808 cites W3034552520 @default.
- W4376602808 cites W3037892792 @default.
- W4376602808 cites W3094775289 @default.
- W4376602808 cites W3100474405 @default.
- W4376602808 cites W3113678014 @default.
- W4376602808 cites W3210535920 @default.
- W4376602808 cites W4205312222 @default.
- W4376602808 cites W4221049914 @default.
- W4376602808 cites W4221098341 @default.
- W4376602808 cites W4230485164 @default.
- W4376602808 cites W4239951476 @default.
- W4376602808 cites W4281902452 @default.
- W4376602808 cites W4283742025 @default.
- W4376602808 cites W4285189757 @default.
- W4376602808 cites W4293222257 @default.
- W4376602808 cites W4294946015 @default.
- W4376602808 doi "https://doi.org/10.1109/access.2023.3276450" @default.
- W4376602808 hasPublicationYear "2023" @default.
- W4376602808 type Work @default.
- W4376602808 citedByCount "2" @default.
- W4376602808 countsByYear W43766028082023 @default.
- W4376602808 crossrefType "journal-article" @default.
- W4376602808 hasAuthorship W4376602808A5002831074 @default.
- W4376602808 hasAuthorship W4376602808A5004039190 @default.
- W4376602808 hasAuthorship W4376602808A5011312647 @default.
- W4376602808 hasAuthorship W4376602808A5042202494 @default.
- W4376602808 hasAuthorship W4376602808A5082398400 @default.
- W4376602808 hasAuthorship W4376602808A5091945978 @default.
- W4376602808 hasBestOaLocation W43766028081 @default.
- W4376602808 hasConcept C101738243 @default.
- W4376602808 hasConcept C111919701 @default.
- W4376602808 hasConcept C118505674 @default.
- W4376602808 hasConcept C153180895 @default.
- W4376602808 hasConcept C154945302 @default.
- W4376602808 hasConcept C34736171 @default.
- W4376602808 hasConcept C41008148 @default.
- W4376602808 hasConcept C50644808 @default.
- W4376602808 hasConceptScore W4376602808C101738243 @default.
- W4376602808 hasConceptScore W4376602808C111919701 @default.
- W4376602808 hasConceptScore W4376602808C118505674 @default.
- W4376602808 hasConceptScore W4376602808C153180895 @default.
- W4376602808 hasConceptScore W4376602808C154945302 @default.
- W4376602808 hasConceptScore W4376602808C34736171 @default.
- W4376602808 hasConceptScore W4376602808C41008148 @default.
- W4376602808 hasConceptScore W4376602808C50644808 @default.
- W4376602808 hasFunder F4320329863 @default.
- W4376602808 hasFunder F4320335440 @default.
- W4376602808 hasLocation W43766028081 @default.
- W4376602808 hasOpenAccess W4376602808 @default.
- W4376602808 hasPrimaryLocation W43766028081 @default.
- W4376602808 hasRelatedWork W2292254049 @default.
- W4376602808 hasRelatedWork W2391959412 @default.
- W4376602808 hasRelatedWork W2592385986 @default.
- W4376602808 hasRelatedWork W2772780115 @default.
- W4376602808 hasRelatedWork W2897995864 @default.
- W4376602808 hasRelatedWork W2998168123 @default.
- W4376602808 hasRelatedWork W3099179464 @default.
- W4376602808 hasRelatedWork W4281924768 @default.
- W4376602808 hasRelatedWork W4287995534 @default.
- W4376602808 hasRelatedWork W4309838615 @default.
- W4376602808 hasVolume "11" @default.
- W4376602808 isParatext "false" @default.
- W4376602808 isRetracted "false" @default.
- W4376602808 workType "article" @default.