ArticleSensors (Basel, Switzerland)2023
A New Semantic Segmentation Framework Based on UNet.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep Learning-Enhanced UV Fluorescence for Automated Detection of Foreign Bodies in Tilapia Fillets.Foods (Basel, Switzerland) · 2026Article
- Contrast-Free Myocardial Infarction Segmentation with Attention U-Net.Diagnostics (Basel, Switzerland) · 2026Article
- [Design and validation of a multimodal model integrating text and imaging data for intelligent assessment of psychological stress in college students].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework.Radiology. Imaging cancer · 2025Article
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2 authors.
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Abstract
This paper discusses a semantic segmentation framework and shows its application in agricultural intelligence, such as providing environmental awareness for agricultural robots to work autonomously and efficiently. We propose an ensemble framework based on the bagging strategy and the UNet network, using RGB and HSV color spaces. We evaluated the framework on our self-built dataset (Maize) and a public dataset (Sugar Beets). Then, we compared it with UNet-based methods (single RGB and single HSV), DeepLab V3+, and SegNet. Experimental results show that our ensemble framework can synthesize the advantages of each color space and obtain the best IoUs (0.8276 and 0.6972) on the datasets (Maize and Sugar Beets), respectively. In addition, including our framework, the UNet-based methods have faster speed and a smaller parameter space than DeepLab V3+ and SegNet, which are more suitable for deployment in resource-constrained environments such as mobile robots.
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