A method of citrus epidermis defects detection based on an improved YOLOv5

Hu, WenXin; Xiong, Juntao; Liang, JunHao; Xie, Zhihong; Liu, Zhiyu; Huang, QiYin; Yang, ZhenGang

doi:10.1016/j.biosystemseng.2023.01.018

Cited by 42 publications

(13 citation statements)

References 27 publications

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“…The neck component plays a critical role in integrating the feature maps generated by various folds in the spine, preparing them for the head segment. According to Hu et al [ 39 ], the neck segment has PAN and FPN structures to improve the network’s feature fusion capabilities. The PAN technology is typically used by YOLOv5, producing three output features: P3, P4, and P5, with dimensions of 80 × 80 × 16, 40 × 40 × 16, and 20 × 20 × 16.…”

Section: The Proposed Methodologymentioning

confidence: 99%

“…The neck component plays a critical role in integrating the feature maps generated by various folds in the spine, preparing them for the head segment. According to Hu et al [39], the neck segment has PAN and FPN structures to improve the network's feature fusion capabilities. The PAN To improve the recognition accuracy for small objects and expand the range of available data, we randomly select the fusion point for merging images.…”

Section: Network Architecturementioning

confidence: 99%

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AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Khan,

Malebary,

Dang

et al. 2024

Plants

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Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

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Section: The Proposed Methodologymentioning

confidence: 99%

“…The neck component plays a critical role in integrating the feature maps generated by various folds in the spine, preparing them for the head segment. According to Hu et al [39], the neck segment has PAN and FPN structures to improve the network's feature fusion capabilities. The PAN To improve the recognition accuracy for small objects and expand the range of available data, we randomly select the fusion point for merging images.…”

Section: Network Architecturementioning

confidence: 99%

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Khan,

Malebary,

Dang

et al. 2024

Plants

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“…Jin et al [28] embedded the GSConv and CBAM modules into YOLOv5 to detect both Gap and Glueless defects among the glass wool dataset. Hu et al [29] optimized the YOLOv5 model by integrating the CBAM module and modifying the loss function, and then applied it to intelligent detection of citrus epidermal defects.…”

Section: Plos Onementioning

confidence: 99%

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

Chen,

Liu,

Wei

et al. 2024

PLoS ONE

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Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving. Both faultless and defective products have similar color and shape at the defect location, making proper inspection challenging. To address these issues, we proposed a defect detection model called PO-YOLOv5 to achieve accurate defect detection for solenoid connectors. First, an additional prediction head was added to enable the model to acquire more semantic information to detect larger-scale defective features. Second, we introduced dynamic convolution to learn complementary connections between the four dimensions of the convolution kernel by utilizing its multidimensional attention mechanism. Replacing conventional convolution with dynamic convolution enhances the detection accuracy of the model and reduces the inference time. Finally, we validated PO-YOLOv5 versus the state-of-the-art object detection methods on the same solenoid connectors dataset. Experiments revealed that our proposed approach exhibited higher accuracy. The mAP (mean Average Precision) result of PO-YOLOv5 was found to be about 90.1%. Compared with the original YOLOv5, PO-YOLOv5 exhibited improved precision by about 3%.

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“…Since its introduction in 2016, YOLO has been updated from the v1 version to the current v8 version, with each generation having optimizations and performance improvements over the previous generation. For example, YOLOV5 [32], based on YOLOV4 [33], introduced mosaic data enhancement, adaptive anchor frame calculation, and adaptive image scaling on the input side. The focus structure and CSP structure have been integrated into the benchmark network, and the FPN+PAN structure has been added to the neck network.…”

Section: Defect Detectionmentioning

confidence: 99%

DG-GAN: A High Quality Defect Image Generation Method for Defect Detection

Luo

et al. 2023

Sensors

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The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and production efficiency. However, in actual production, it is often difficult to collect defect image samples. Without a sufficient number of defect image samples, training defect detection models is difficult to achieve. In this paper, a defect image generation method DG-GAN is proposed for defect detection. Based on the idea of the progressive generative adversarial, D2 adversarial loss function, cyclic consistency loss function, a data augmentation module, and a self-attention mechanism are introduced to improve the training stability and generative ability of the network. The DG-GAN method can generate high-quality and high-diversity surface defect images. The surface defect image generated by the model can be used to train the defect detection model and improve the convergence stability and detection accuracy of the defect detection model. Validation was performed on two data sets. Compared to the previous methods, the FID score of the generated defect images was significantly reduced (mean reductions of 16.17 and 20.06, respectively). The YOLOX detection accuracy was significantly improved with the increase in generated defect images (the highest increases were 6.1% and 20.4%, respectively). Experimental results showed that the DG-GAN model is effective in surface defect detection tasks.

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A method of citrus epidermis defects detection based on an improved YOLOv5

Cited by 42 publications

References 27 publications

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

DG-GAN: A High Quality Defect Image Generation Method for Defect Detection

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