Sim-YOLOv5s: A method for detecting defects on the end face of lithium battery steel shells

Hu, Haibing; Zhu, Zhenhao

doi:10.1016/j.aei.2022.101824

Cited by 23 publications

(8 citation statements)

References 22 publications

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“…It reduces the amount of computation by three consecutive maximum pooling, the convolution kernel unified as 5*5, finally concat the results before pooling and after each pooling, meanwhile ensures the effect of multi-scale fusion achieves the fusion of local features and global features at the level of featherMap ( Tang et al., 2023 ). In the citrus young fruit dataset there was some noise and interference, so in order to improve the robustness of the model, the SimSPPF structure ( Hu and Zhu, 2023 ; Wang et al., 2023 ) was introduced and the large kernel separated attention (LSKA) was used in the architecture, which was called the feature pyramid structure as SimSPPF- LSKA, and the structure as shown in Figure 9 .…”

Section: Experiments and Methodsmentioning

confidence: 99%

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Ang,

Zhiwei,

Wei

et al. 2024

Front. Plant Sci.

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In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

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Section: Experiments and Methodsmentioning

confidence: 99%

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Ang,

Zhiwei,

Wei

et al. 2024

Front. Plant Sci.

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“…The above method uses lightweight modules to redesign the backbone feature extraction network of YOLOv4, achieving the real-time detection of defective targets. Hu et al [24] embedded the CBAM attention module in the backbone network of YOLOv5 and proposed a fast spatial pooling pyramid structure, SimSPPF, to speed up the operation of the model and reduce the amount of computation while improving the feature extraction capability of the model. Lan et al [25] used the lightweight Ghost module as the backbone network of YOLOv5 and embedded the CBAM attention mechanism into the neck network to improve the detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Hu et al [24] 2023 A fast spatial pooling pyramid structure (SimSPPF) was proposed to speed up the operation of YOLOv5.…”

Section: Related Workmentioning

confidence: 99%

STMS-YOLOv5: A Lightweight Algorithm for Gear Surface Defect Detection

Yan,

Zhang,

Bai

et al. 2023

Sensors

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Most deep-learning-based object detection algorithms exhibit low speeds and accuracy in gear surface defect detection due to their high computational costs and complex structures. To solve this problem, a lightweight model for gear surface defect detection, namely STMS-YOLOv5, is proposed in this paper. Firstly, the ShuffleNetv2 module is employed as the backbone to reduce the giga floating-point operations per second and the number of parameters. Secondly, transposed convolution upsampling is used to enhance the learning capability of the network. Thirdly, the max efficient channel attention mechanism is embedded in the neck to compensate for the accuracy loss caused by the lightweight backbone. Finally, the SIOU_Loss is adopted as the bounding box regression loss function in the prediction part to speed up the model convergence. Experiments show that STMS-YOLOv5 achieves frames per second of 130.4 and 133.5 on the gear and NEU-DET steel surface defect datasets, respectively. The number of parameters and GFLOPs are reduced by 44.4% and 50.31%, respectively, while the mAP@0.5 reaches 98.6% and 73.5%, respectively. Extensive ablation and comparative experiments validate the effectiveness and generalization capability of the model in industrial defect detection.

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“…Haibing Hu proposed a Sim -YOLOv5s algorithm for lithium battery shell defect detection. By embedding the attention mechanism in the backbone network, the recognition accuracy of the algorithm can reach 88.3% [2]. Harshad K. Dandage proposed a LSDD method for multi-scale image enhancement and classification.…”

Section: Related Workmentioning

confidence: 99%

Research on Artificial Intelligence Detection Method of Lithium Battery Surface Defects for Production Line

Wang¹,

Wang²,

Fan³

et al. 2023

Journal of Computers

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<p>Lithium batteries are widely used in new energy vehicles and electronic equipment. Aiming at the typical defects that are easy to occur in the production process of lithium batteries, this paper improves the performance and recognition accuracy of the algorithm by integrating void convolution and attention mechanism into the YOLOv5 basic framework. At the same time, whale algorithm is used to automatically optimize the algorithm parameters in the process of optimization. Finally, through simulation experiments. This method realizes the rapid and accurate identification of lithium battery defects in the rapid production process of automatic production line.</p> <p> </p>

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Sim-YOLOv5s: A method for detecting defects on the end face of lithium battery steel shells

Cited by 23 publications

References 22 publications

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

STMS-YOLOv5: A Lightweight Algorithm for Gear Surface Defect Detection

Research on Artificial Intelligence Detection Method of Lithium Battery Surface Defects for Production Line

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