A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Yang, Guoliang; Wang, Jixiang; Nie, Zaiping; Yang, Hao; Yu, Senhua

doi:10.3390/agronomy13071824

Cited by 84 publications

(27 citation statements)

References 31 publications

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“…Additionally, the model adopted an anchor-free strategy, which eliminates the need for predefined anchor boxes by directly predicting the bounding box coordinates, leading to a simpler and more flexible detection framework. In terms of loss functions, YOLOv8 uses binary cross entropy loss function in the classification branch, and distribution focus loss function and CloU Loss Function in the regression branch [37].…”

Section: High-precision Rail Flaw Detection Methods Based On Yolov8mentioning

confidence: 99%

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Yu,

Liu,

Cao

et al. 2024

Meas. Sci. Technol.

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With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the FPS is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.

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Section: High-precision Rail Flaw Detection Methods Based On Yolov8mentioning

confidence: 99%

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Yu,

Liu,

Cao

et al. 2024

Meas. Sci. Technol.

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“…To further validate the efficacy of YOLOv5s-MGNS, we conducted comparative experiments involving the latest YOLOv8 network, a lightweight variant of YOLOv8, and an improved YOLOv8s network proposed by Yang Guoliang et al 24 (YOLOv8s + DSConv + DPAG + FEM, abbreviated as YOLOv8s_DDF). The comparative results are outlined in Table 6.…”

Section: Comparison Test Of Different Network Modelsmentioning

confidence: 99%

Research on recognition algorithm for special-shaped parts based on improved YOLOv5s

Zhang,

Guo,

Yang

2024

J. Electron. Imag.

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With the rapid development of machinery and equipment modernization, more and more non-standard shaped parts are designed and put into specific occasions to use to meet the needs of special circumstances. Therefore, how to quickly recognize the shaped parts has become an urgent need for a technology. To recognize shaped parts, deep learning methods such as the widely used YOLOv5s network are commonly employed. However, directly deploying the official network model has drawbacks, including heavy reliance on data, poor detection results for small target objects, and high hardware requirements. These issues increase the threshold for non-professionals to use it. For this reason, this paper designs an improved network based on YOLOv5s. This paper proposes improvements in terms of both lightness and accuracy. In terms of light weight, the backbone of YOLOv5s is replaced by MobileNetV3; and the convolution and C3 module of the head part of YOLOv5s is replaced by phantom convolution and C3Ghost module, and the attention mechanism layer is trimmed to reduce the number of computational parameters and model size. In terms of accuracy, non-maximum suppression (NMS) is improved to Soft-NMS; intersection over union (IoU) loss function is replaced with distance-IoU loss function. And trained on the homemade shaped parts dataset, the results show that the average accuracy of the improved network is 99.2% in the test case, the model size is 2.4M, and the detection time is 1.5 ms per image, which is a significant increase in speed and accuracy compared with other unmodified networks, and a substantial decrease in the model size and the number of parameters.

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“…Depthwise Separable Convolution (DSConv) is a convolution operation within Convolutional Neural Networks (CNN), and its core idea involves decomposing the traditional convolution into two steps: depthwise convolution and pointwise convolution (Yang et al, 2023). This decomposition process aids in reducing computational and parameter requirements, thereby lowering the model's complexity and improving computational e ciency.…”

Section: Depthwise Separable Convolutionmentioning

confidence: 99%

Enhancing Real-time Target Detection in Smart Cities: YOLOv8-DSAF Insights

Li,

Huang,

Tao

2024

Preprint

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With the global rise of smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF. The model comprises three key modules: Depthwise Separable Convolution (DSConv), Dual-Path Attention Gate module (DPAG), and Feature Enhancement Module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

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A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Cited by 84 publications

References 31 publications

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

Research on recognition algorithm for special-shaped parts based on improved YOLOv5s

Enhancing Real-time Target Detection in Smart Cities: YOLOv8-DSAF Insights

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