A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

Zhang, Jing; Wang, Jun; Zhao, M. G.

doi:10.3390/agronomy13071779

Cited by 11 publications

(3 citation statements)

References 52 publications

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“…In recent years, with the rapid development of deep learning technology, many detection methods based on deep learning have been widely used in various scenarios, including materials [6], medical [7], agriculture [8], transportation [9], textile [10] and other fields. Especially in the industrial sector, deep learning-based surface defect detection has become a popular application.…”

Section: Introductionmentioning

confidence: 99%

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

Yang,

Zuo,

et al. 2024

Meas. Sci. Technol.

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The fluorescent magnetic particle inspection technique is often used for surface crack detection of bearing rings due to its advantages of simple operation and high sensitivity. With the development of computer vision technology, more and more visual algorithms are used in magnetic particle inspection for defect detection. However, most of these current algorithm models have low detection accuracy and poor efficiency, making it difficult to meet the precision requirements of production testing and affecting the overall pace of production processes. To address this problem, this paper proposes an improved algorithm model based on Yolov5. Firstly, MobileNetV3-small is utilized to construct the backbone feature extraction network, reducing the network's parameter count and enhancing its detection speed. In addition, BiFPN is implemented to facilitate swift and efficient multi-scale feature fusion, while the C3 module in the neck is replaced with C2f to enhance detection precision. Finally, Focal-Loss EIoU is adopted as the loss function to improve the model's accuracy in positioning the crack borders. Experimental results demonstrate that the precision of this model in detecting surface cracks in bearing rings achieves an impressive 95.1%, while the recall reaches 90.4%. The mAP stands at 0.946. When compared to the original Yolov5s network, this model showcases a reduction in network parameters by 32.1% and a significant increase in FPS by 40.0%. These improvements effectively fulfill the production process's demands for crack detection tasks, providing a balance between accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

Yang,

Zuo,

et al. 2024

Meas. Sci. Technol.

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“…With the continuous development of deep learning technology, it has shown great application potential in several fields. Its applications have been extended to IoT security detection, management of traffic congestion problems at urban intersections [7], supply chain management procurement, inventory control, Wi-Fi channel state information to recognize human activities [8], tomato identification and localization [9], real-time detection of crop pests and diseases [10][11][12], forest fire smoke detection to optimize the efficiency of agricultural operations, classification of sonar images [13], and effects of electromagnetic hydrodynamics on nano-viscous fluid flow [14]. These studies not only highlight the promise of deep learning techniques in various fields but also demonstrate the value of their wide range of applications in the agricultural industry.…”

Section: Introductionmentioning

confidence: 99%

TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention

Chen,

Zhou,

Lin

et al. 2024

Agronomy

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The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.

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“…In recent years, deep learning has a wide range of application prospects in the intelligent identification and forecasting of crop pests and diseases, mainly the two-stage target detection models of the R-CNN [4][5][6][7] series and the single-stage target detection models of the YOLO [8][9][10][11] and SSD [12][13][14] series. Prakruti et al [15] used the YOLOv3 model to identify and localize diseases on tea leaves, and trained tea disease images with different resolutions, qualities, brightness, and focus, using a rich dataset of disease images with an average accuracy mean of 86%.…”

Section: Introductionmentioning

confidence: 99%

Research on Insect Pest Identification in Rice Canopy Based on GA-Mask R-CNN

Liu,

Fu,

et al. 2023

Agronomy

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Aiming at difficult image acquisition and low recognition accuracy of two rice canopy pests, rice stem borer and rice leaf roller, we constructed a GA-Mask R-CNN (Generative Adversarial Based Mask Region Convolutional Neural Network) intelligent recognition model for rice stem borer and rice leaf roller, and we combined it with field monitoring equipment for them. Firstly, based on the biological habits of rice canopy pests, a variety of rice pest collection methods were used to obtain the images of rice stem borer and rice leaf roller pests. Based on different segmentation algorithms, the rice pest images were segmented to extract single pest samples. Secondly, the bug generator based on a generative adversarial network strategy improves the sensitivity of the classification network to the bug information, generates the pest information images in the real environment, and obtains the sample dataset for deep learning through multi-way augmentation. Then, through adding channel attention ECA module in Mask R-CNN and improving the connection of residual blocks in the backbone network ResNet101, the recognition accuracy of the model is improved. Finally, the GA-Mask R-CNN model was tested on a multi-source dataset with an average precision (AP) of 92.71%, recall (R) of 89.28% and a balanced score F1 of 90.96%. The average precision, recall, and balanced score F1 are improved by 7.07, 7.65, and 8.83%, respectively, compared to the original Mask R-CNN. The results show that the GA-Mask R-CNN model performance indexes are all better than the Mask R-CNN, the Faster R-CNN, the SSD, the YOLOv5, and other network models, which can provide technical support for remote intelligent monitoring of rice pests.

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A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

Cited by 11 publications

References 52 publications

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention

Research on Insect Pest Identification in Rice Canopy Based on GA-Mask R-CNN

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