ArticleSensors (Basel, Switzerland)2022
Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- FogGate-YOLO: Traffic Object Detection in Foggy Environments Using Channel Selection Mechanisms.Sensors (Basel, Switzerland) · 2026Article
- A Fast and Real-Time Machine Vision Evaluation System for Size Grading ofFood science & nutrition · 2026Article
- YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s.Sensors (Basel, Switzerland) · 2023Article
- YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios.Sensors (Basel, Switzerland) · 2023Article
- Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network.Sensors (Basel, Switzerland) · 2023Article
- A Lightweight Algorithm for Insulator Target Detection and Defect Identification.Sensors (Basel, Switzerland) · 2023Article
- Article
- A lightweight network for improving wheat ears detection and counting based on YOLOv5s.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In the foxtail millet field, due to the dense distribution of the foxtail millet ears, morphological differences among foxtail millet ears, severe shading of stems and leaves, and complex background, it is difficult to identify the foxtail millet ears. To solve these practical problems, this study proposes a lightweight foxtail millet ear detection method based on improved YOLOv5. The improved model proposes to use the GhostNet module to optimize the model structure of the original YOLOv5, which can reduce the model parameters and the amount of calculation. This study adopts an approach that incorporates the Coordinate Attention (CA) mechanism into the model structure and adjusts the loss function to the Efficient Intersection over Union (EIOU) loss function. Experimental results show that these methods can effectively improve the detection effect of occlusion and small-sized foxtail millet ears. The recall, precision, F
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