FL-YOLOv8: Lightweight Object Detector Based on Feature Fusion Article Swipe
Ying Xue
,
Qijin Wang
,
Yating Hu
,
Qian Yu
,
Long Cheng
,
Hongqiang Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13234653
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13234653
In recent years, anchor-free object detectors have become predominant in deep learning, the YOLOv8 model as a real-time object detector based on anchor-free frames is universal and influential, it efficiently detects objects across multiple scales. However, the generalization performance of the model is lacking, and the feature fusion within the neck module overly relies on its structural design and dataset size, and it is particularly difficult to localize and detect small objects. To address these issues, we propose the FL-YOLOv8 object detector, which is improved based on YOLOv8s. Firstly, we introduce the FSDI module in the neck, enhancing semantic information across all layers and incorporating rich detailed features through straightforward layer-hopping connections. This module integrates both high-level and low-level information to enhance the accuracy and efficiency of image detection. Meanwhile, the structure of the model was optimized and designed, and the LSCD module is constructed in the detection head; adopting a lightweight shared convolutional detection head reduces the number of parameters and computation of the model by 19% and 10%, respectively. Our model achieves a comprehensive performance of 45.5% on the COCO generalized dataset, surpassing the benchmark by 0.8 percentage points. To further validate the effectiveness of the method, experiments were also performed on specific domain urine sediment data (FCUS22), and the results on category detection also better justify the FL-YOLOv8 object detection algorithm.
Related Topics
Concepts
Computer science
Object detection
Benchmark (surveying)
Detector
Feature (linguistics)
Generalization
Object (grammar)
Artificial intelligence
Domain (mathematical analysis)
Computation
Pattern recognition (psychology)
Feature extraction
Computer vision
Data mining
Algorithm
Mathematics
Geography
Linguistics
Geodesy
Telecommunications
Philosophy
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13234653
- OA Status
- gold
- Cited By
- 3
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404691340
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404691340Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13234653Digital Object Identifier
- Title
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FL-YOLOv8: Lightweight Object Detector Based on Feature FusionWork title
- Type
-
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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2024-11-25Full publication date if available
- Authors
-
Ying Xue, Qijin Wang, Yating Hu, Qian Yu, Long Cheng, Hongqiang WangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics13234653Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/electronics13234653Direct OA link when available
- Concepts
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Computer science, Object detection, Benchmark (surveying), Detector, Feature (linguistics), Generalization, Object (grammar), Artificial intelligence, Domain (mathematical analysis), Computation, Pattern recognition (psychology), Feature extraction, Computer vision, Data mining, Algorithm, Mathematics, Geography, Linguistics, Geodesy, Telecommunications, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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