Lightweight Small Target Detection Algorithm with Multi-Feature Fusion

Yang, Rujin; Zhang, Jingwei; Shang, Xinna; Li, Wenfa; Zhu, Deping

doi:10.2139/ssrn.4449863

2023

DOI: 10.2139/ssrn.4449863

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Lightweight Small Target Detection Algorithm with Multi-Feature Fusion

Rujin Yang

Jingwei Zhang

Xinna Shang

et al.

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2024

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Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

Yeh,

Lin,

Yuan

et al. 2024

Electronics

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Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture.

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Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

Yeh,

Lin,

Yuan

et al. 2024

Electronics

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An Improved YOLOv5 Algorithm for Bamboo Strip Defect Detection Based on the Ghost Module

Yang,

Lee,

Lee

et al. 2024

Forests

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Detecting surface defects in bamboo strips is essential for producing Asian bamboo products. Currently, the detection of surface defects in bamboo strips mainly relies on manual labor. The labor intensity is high, and the detection efficiency is low. Improving the speed and accuracy of identifying bamboo strip defects is crucial in enhancing enterprises’ production efficiency. Hence, this research designs a lightweight YOLOv5s neural network algorithm using the Ghost module to identify surface defects of bamboo strips. The research introduces an attention mechanism CA module to improve the recognition ability of the model target; the research also implements a C2f model to enhance the network performance and the surface quality of bamboo strips. The experimental results show that after training with the acquired image dataset, the YOLOv5s model can exert an intelligent detection effect on five common types of defects in bamboo strips, and the Ghost module makes YOLOv5s lightweight, which can effectively reduce model parameters and improve detection speed while maintaining recognition accuracy. Meanwhile, the C2f module and CA module can further leverage the model’s ability to identify specific defects in bamboo strips after lightweight improvement.

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Lightweight Small Target Detection Algorithm with Multi-Feature Fusion

Cited by 2 publications

References 0 publications

Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

An Improved YOLOv5 Algorithm for Bamboo Strip Defect Detection Based on the Ghost Module

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