Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

Duan, Xingwei; Lin, Yuhao; Li, Lixia; Zhang, Fujie; Li, Shanshan; Liao, Yuxin

doi:10.3390/agronomy13061477

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The standard convolution floating-point operation is denoted as n × h′× ω′ × c × k × k, wherein c represents the number of input channels. In contrast, the Ghost convolution combines m(s − 1) = n/s + (s − 1) linear computations [18] with the standard convolution. The linear transformation convolves the kernel of size d × d. Hence, the computational ratio between the two can be expressed as Formula (3).…”

Section: Backbone Section Introduces Lightweight Ghostnet Modulementioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

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Section: Backbone Section Introduces Lightweight Ghostnet Modulementioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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Hazard Detection for Power Transmission Channel Based on Texture Enhancement and Context Awareness

Chen,

Huang,

Lin

et al. 2024

2024 43rd Chinese Control Conference (CCC)

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SA-SRYOLOv8: A Research on Star Anise Variety Recognition Based on a Lightweight Cascaded Neural Network and Diversified Fusion Dataset

Chen,

Zhang,

Guo

et al. 2024

Agronomy

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Star anise, a widely popular spice, benefits from classification that enhances its economic value. In response to the low identification efficiency and accuracy of star anise varieties in the market, as well as the scarcity of related research, this study proposes an efficient identification method based on non-similarity augmentation and a lightweight cascaded neural network. Specifically, this approach utilizes a Siamese enhanced data network and a front-end SRGAN network to address sample imbalance and the challenge of identifying blurred images. The YOLOv8 model is further lightweight to reduce memory usage and increase detection speed, followed by optimization of the weight parameters through an extended training strategy. Additionally, a diversified fusion dataset of star anise, incorporating open data, was constructed to further validate the feasibility and effectiveness of this method. Testing showed that the SA-SRYOLOv8 detection model achieved an average detection precision (mAP) of 96.37%, with a detection speed of 146 FPS. Ablation experiment results showed that compared to the original YOLOv8 and the improved YOLOv8, the cascade model’s mAP increased by 0.09 to 0.81 percentage points. Additionally, when compared to mainstream detection models such as SSD, Fast R-CNN, YOLOv3, YOLOv5, YOLOX, and YOLOv7, the cascade model’s mAP increased by 1.81 to 19.7 percentage points. Furthermore, the model was significantly lighter, at only about 7.4% of the weight of YOLOv3, and operated at twice the speed of YOLOv7. Visualization results demonstrated that the cascade model accurately detected multiple star anise varieties across different scenarios, achieving high-precision detection targets. The model proposed in this study can provide new theoretical frameworks and ideas for constructing real-time star anise detection systems, offering new technological applications for smart agriculture.

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Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

Cited by 3 publications

References 30 publications

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Hazard Detection for Power Transmission Channel Based on Texture Enhancement and Context Awareness

SA-SRYOLOv8: A Research on Star Anise Variety Recognition Based on a Lightweight Cascaded Neural Network and Diversified Fusion Dataset

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