Automatic Counting Method for Centipedes Based on Deep Learning Article Swipe
Jin Yao
,
Weitao Chen
,
Tao Wang
,
Fu Yang
,
Xiaoyan Sun
,
Chong Yao
,
Liangquan Jia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3414114
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3414114
The utilization of target detection algorithms for counting edible centipedes represents a novel endeavor in the field of traditional Chinese medicine materials using deep learning. However, the accuracy of current target detection algorithms is relatively low due to the complexity of the centipede background and the density of the detection targets, making them poorly suited for practical application scenarios. To address this, this study proposes a centipede target detection algorithm based on an improved Your Only Look Once V5 (YOLOv5) model, termed FD-YOLO. This algorithm enhances the original model by incorporating the CBAM attention mechanism and the BiFormer universal visual transformer to suppress irrelevant information and intensify focus on the desired detection targets, thereby improving the precision of the algorithm. Additionally, the FD-YOLO algorithm enhances the model’s generalizability and robustness by improving the existing SPPF module. Experimental results demonstrate that compared to the original YOLOv5 prototype network, the improved YOLOv5 model has increased the [email protected] by 3.5%, reaching 97.2%, and raised the recall rate from 86.2% to 92.9%. Therefore, the enhanced YOLOv5 algorithm can effectively detect and count centipedes, making it more suitable for current practical application scenarios.
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All OpenAlex metadata
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https://doi.org/10.1109/access.2024.3414114Digital Object Identifier
- Title
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Automatic Counting Method for Centipedes Based on Deep LearningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Jin Yao, Weitao Chen, Tao Wang, Fu Yang, Xiaoyan Sun, Chong Yao, Liangquan JiaList of authors in order
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https://doi.org/10.1109/access.2024.3414114Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3414114Direct OA link when available
- Concepts
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.count | 177 |
| abstract_inverted_index.field | 16 |
| abstract_inverted_index.focus | 107 |
| abstract_inverted_index.model | 88, 150 |
| abstract_inverted_index.novel | 12 |
| abstract_inverted_index.study | 63 |
| abstract_inverted_index.this, | 61 |
| abstract_inverted_index.using | 22 |
| abstract_inverted_index.YOLOv5 | 144, 149, 171 |
| abstract_inverted_index.detect | 175 |
| abstract_inverted_index.edible | 8 |
| abstract_inverted_index.making | 51, 179 |
| abstract_inverted_index.model, | 80 |
| abstract_inverted_index.poorly | 53 |
| abstract_inverted_index.raised | 160 |
| abstract_inverted_index.recall | 162 |
| abstract_inverted_index.suited | 54 |
| abstract_inverted_index.target | 3, 30, 67 |
| abstract_inverted_index.termed | 81 |
| abstract_inverted_index.visual | 99 |
| abstract_inverted_index.Chinese | 19 |
| abstract_inverted_index.FD-YOLO | 122 |
| abstract_inverted_index.address | 60 |
| abstract_inverted_index.current | 29, 184 |
| abstract_inverted_index.density | 46 |
| abstract_inverted_index.desired | 110 |
| abstract_inverted_index.module. | 135 |
| abstract_inverted_index.results | 137 |
| abstract_inverted_index.thereby | 113 |
| abstract_inverted_index.(YOLOv5) | 79 |
| abstract_inverted_index.BiFormer | 97 |
| abstract_inverted_index.FD-YOLO. | 82 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.accuracy | 27 |
| abstract_inverted_index.compared | 140 |
| abstract_inverted_index.counting | 7 |
| abstract_inverted_index.endeavor | 13 |
| abstract_inverted_index.enhanced | 170 |
| abstract_inverted_index.enhances | 85, 124 |
| abstract_inverted_index.existing | 133 |
| abstract_inverted_index.improved | 73, 148 |
| abstract_inverted_index.medicine | 20 |
| abstract_inverted_index.network, | 146 |
| abstract_inverted_index.original | 87, 143 |
| abstract_inverted_index.proposes | 64 |
| abstract_inverted_index.reaching | 157 |
| abstract_inverted_index.suitable | 182 |
| abstract_inverted_index.suppress | 102 |
| abstract_inverted_index.targets, | 50, 112 |
| abstract_inverted_index.algorithm | 69, 84, 123, 172 |
| abstract_inverted_index.attention | 93 |
| abstract_inverted_index.centipede | 42, 66 |
| abstract_inverted_index.detection | 4, 31, 49, 68, 111 |
| abstract_inverted_index.improving | 114, 131 |
| abstract_inverted_index.increased | 152 |
| abstract_inverted_index.intensify | 106 |
| abstract_inverted_index.learning. | 24 |
| abstract_inverted_index.materials | 21 |
| abstract_inverted_index.mechanism | 94 |
| abstract_inverted_index.practical | 56, 185 |
| abstract_inverted_index.precision | 116 |
| abstract_inverted_index.prototype | 145 |
| abstract_inverted_index.universal | 98 |
| abstract_inverted_index.Therefore, | 168 |
| abstract_inverted_index.algorithm. | 119 |
| abstract_inverted_index.algorithms | 5, 32 |
| abstract_inverted_index.background | 43 |
| abstract_inverted_index.centipedes | 9 |
| abstract_inverted_index.complexity | 39 |
| abstract_inverted_index.irrelevant | 103 |
| abstract_inverted_index.relatively | 34 |
| abstract_inverted_index.represents | 10 |
| abstract_inverted_index.robustness | 129 |
| abstract_inverted_index.scenarios. | 58, 187 |
| abstract_inverted_index.application | 57, 186 |
| abstract_inverted_index.centipedes, | 178 |
| abstract_inverted_index.demonstrate | 138 |
| abstract_inverted_index.effectively | 174 |
| abstract_inverted_index.information | 104 |
| abstract_inverted_index.traditional | 18 |
| abstract_inverted_index.transformer | 100 |
| abstract_inverted_index.utilization | 1 |
| abstract_inverted_index.3.5%, | 156 |
| abstract_inverted_index.86.2% | 165 |
| abstract_inverted_index.Experimental | 136 |
| abstract_inverted_index.92.9%. | 167 |
| abstract_inverted_index.97.2%, | 158 |
| abstract_inverted_index.AP@0.5 | 154 |
| abstract_inverted_index.Additionally, | 120 |
| abstract_inverted_index.incorporating | 90 |
| abstract_inverted_index.model’s | 126 |
| abstract_inverted_index.generalizability | 127 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.15361268 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |