Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm

Qiu, Yang; Ai, Zhijie; Yang, Lin; Xu, Zhezhuang; Liu, Xinxiang

doi:10.1007/978-981-16-6320-8_53

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…By comparing the experimental results in this research with Qiu et al (2022), it can be seen that the average detection accuracy of the algorithm is 12.46% higher than that in Qiu et al (2022), but the detection speed of the algorithm is 39.73 frames/s lower than that in Qiu et al (2022). This is because Qiu et al (2022) uses the YOLOV4tiny algorithm, a simplified version of the YOLOv4 algorithm, for flaw detection. The YOLOV4-tiny algorithm is a lightweight algorithm with fast detection speed but relatively low detection accuracy.…”

Section: Contrast Experimentsmentioning

confidence: 88%

“…In Table 1 , Qiu et al (2022) and Yang and Sang (2022) are the two latest research studies selected for flaw detection using YOLOv4, among which ( Qiu et al, 2022 ) uses the improved YOLOV4-tiny algorithm for wood panel flaw detection. Flaw detection using the YOLOV4-tiny algorithm greatly improves the detection speed.…”

Section: Methodsmentioning

confidence: 99%

“… Liao et al (2021 ) proposed an algorithm for PCB surface flaw detection based on improved YOLOv4, which achieved a detection speed of 56.98 frames/s without detection and improved detection accuracy. Qiu et al (2022 ) proposed an improved YOLOv4-tiny algorithm for flaw detection of wood panels. Res2Net was used as the backbone feature extraction network, resulting in average detection accuracy of the algorithm up to 80.1%, and the detection speed up to 76.9 frames/s.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flaw Detection in White Porcelain Wine Bottles Based on Improved YOLOv4 Algorithm

Gong

Huang

Wang

2022

Front. Bioeng. Biotechnol.

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Aiming at the problems of low detection accuracy and slow detection speed in white porcelain wine bottle flaw detection, an improved flaw detection algorithm based on YOLOv4 was proposed. By adding Coordinate Attention to the backbone feature extraction network, the extracting ability of white porcelain bottle flaw features was improved. Deformable convolution is added to locate flaws more accurately, so as to improve the detection accuracy of flaws by the model. Efficient Intersection over Union was used to replace Complete Intersection over Union in YOLOv4 to improve the loss function and improve the model detection speed and accuracy. Experimental results on the surface flaw data set of white porcelain wine bottles show that the proposed algorithm can effectively detect white porcelain wine bottle flaws, the mean Average Precision of the model can reach 92.56%, and the detection speed can reach 37.17 frames/s.

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Section: Contrast Experimentsmentioning

confidence: 88%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flaw Detection in White Porcelain Wine Bottles Based on Improved YOLOv4 Algorithm

Gong

Huang

Wang

2022

Front. Bioeng. Biotechnol.

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“…Due to the late introduction of YoloX, Yolov6, and Yolov7 algorithms, there has been no research on fruit detection in the literature review. [6] In addition, deep learning is also widely used in fruit detection tasks. Liu Xiaogang et al prepared for strawberry picking and used various deep learning methods to detect strawberries in natural environments.…”

Section: Theoretical Basis Of Yolo Series Algorithmsmentioning

confidence: 99%

Research on YOLOV7-tiny fruit detection algorithm improved by α-IoU

JING,

ZHU

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In response to the problems of low detection accuracy and inaccurate positioning in traditional YOLO algorithms for fruit detection tasks, this paper proposes an improved method -replacing the original loss function of YOLOV7 with the AlphaIoU loss function, and optimizing the boundary box regression of the model. This can accelerate the convergence speed of the model and improve training accuracy. Through experimental verification, it was found that the improved YOLOV7-tiny model significantly improved the average accuracy, accuracy, and recall in fruit detection tasks compared to the original network. This indicates that this method has good performance in solving the problems of traditional YOLO algorithm, providing better performance and accuracy for fruit detection tasks, and contributing to the further development of applications and research in related fields.

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ICDW-YOLO: An Efficient Timber Construction Crack Detection Algorithm

Zhou,

Ning,

Xiang

et al. 2024

Sensors

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A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model’s recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather–distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50–95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm’s effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50–95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors.

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Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm

Cited by 3 publications

References 12 publications

Flaw Detection in White Porcelain Wine Bottles Based on Improved YOLOv4 Algorithm

Flaw Detection in White Porcelain Wine Bottles Based on Improved YOLOv4 Algorithm

Research on YOLOV7-tiny fruit detection algorithm improved by α-IoU

ICDW-YOLO: An Efficient Timber Construction Crack Detection Algorithm

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