Forestry Pests Identification and Classification Based on Improved YOLO v5s

Zhang, Lijie; Yin, Liyuan; Liu, Luyang; Zhuo, Ran; Zhuo, Yujan

doi:10.1109/eiecs53707.2021.9588027

2021 International Conference on Electronic Information Engineering and Computer Science (EIECS) 2021

DOI: 10.1109/eiecs53707.2021.9588027

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Forestry Pests Identification and Classification Based on Improved YOLO v5s

Lijie Zhang

Liyuan Yin

Luyang Liu

et al.

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Cited by 2 publications

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HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT

Zeng,

Chen,

Liu

et al. 2024

Agronomy

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Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network’s attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model’s parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer’s efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control.

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HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT

Zeng,

Chen,

Liu

et al. 2024

Agronomy

View full text Add to dashboard Cite

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Application of YOLO and ResNet in Heat Staking Process Inspection

Jung

Rhee²

2022

Sustainability

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In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework, and a classification algorithm, residual network (ResNet), to a real heat staking process image dataset. The study leverages the advantages of YOLO models and ResNet to increase the overall efficiency and accuracy of detecting heat staking points from door trim images and classify whether the detected heat staking points are defected or not. The proposed model achieved high accuracy in both object detection (mAP of 95.1%) and classification (F1-score of 98%). These results show that the developed deep learning models can be applied to the real-time inspection of the heat staking process. The models can increase productivity and quality while decreasing human labor cost, ultimately improving a firm’s competitiveness.

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Forestry Pests Identification and Classification Based on Improved YOLO v5s

Cited by 2 publications

References 12 publications

HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT

HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT

Application of YOLO and ResNet in Heat Staking Process Inspection

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