Defective Edge Detection System Design for Long Bamboo Batten

Sun, Tong; Mou, Xiangwei; Song, Shuxiang

doi:10.2991/eia-17.2017.53

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Furthermore, some methods have been proposed for specific types of defects in bamboo strips. The defective edge detection system designed by Sun et al focuses on the detection of edge defects in bamboo [15]. The system uses an optical fiber amplifier to detect the intensity of light leaking from the gap between the contact plate and the edge of the bamboo strip.…”

Section: Iirelated Workmentioning

confidence: 99%

Improved YOLOV4-CSP Algorithm for Detection of Bamboo Surface Sliver Defects With Extreme Aspect Ratio

et al. 2022

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Bamboo surface defect detection provides quality assurance for bamboo product manufacture in industrial scenarios, an integral part of the overall manufacturing process. Currently, bamboo defect inspection predominantly relies on manual operation, but manual inspection is very time-consuming as well as labor-intensive, and the quality of inspection is not guaranteed. In recent years, a few visual inspection systems based on traditional image processing have been deployed in some factories. However, traditional machine vision algorithms extract features in tedious steps and have poor performance along with poor adaptability in the face of complex defects. Accordingly, many scholars are committed to seeking deep learning methods to accomplish surface defect detection. However, existing deep learning object detectors struggle with specific industrial defects when directly applied to industrial defect detection, such as sliver defects, especially for ones with extreme aspect ratios. To this end, this paper proposes an improved model based on the advanced object detector YOLOV4-CSP, which introduces asymmetric convolution and attention mechanism. The introduction of asymmetric convolution enhances the feature extraction in the horizontal direction of the bamboo strip surface, improving the performance in detecting sliver defects. In addition, convolutional block attention module(CBAM), a hybrid attention module, which combines channel attention with spatial attention, is utilized to promote the representation ability of the model by increasing the weights of crucial channels and regions. The proposed model achieves outstanding performance in the general categories and excels in the hard-to-detect categories. Some enterprise's bamboo strip dataset experiments verify that the model can reach 96.74% mAP for the typical six surface defects. Meanwhile, we also observe significant improvements when extending our model to aluminum datasets with similar characteristics.

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Section: Iirelated Workmentioning

confidence: 99%

Improved YOLOV4-CSP Algorithm for Detection of Bamboo Surface Sliver Defects With Extreme Aspect Ratio

et al. 2022

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An Improved YOLOv5 Algorithm for Bamboo Strip Defect Detection Based on the Ghost Module

Yang,

Lee,

Lee

et al. 2024

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Detecting surface defects in bamboo strips is essential for producing Asian bamboo products. Currently, the detection of surface defects in bamboo strips mainly relies on manual labor. The labor intensity is high, and the detection efficiency is low. Improving the speed and accuracy of identifying bamboo strip defects is crucial in enhancing enterprises’ production efficiency. Hence, this research designs a lightweight YOLOv5s neural network algorithm using the Ghost module to identify surface defects of bamboo strips. The research introduces an attention mechanism CA module to improve the recognition ability of the model target; the research also implements a C2f model to enhance the network performance and the surface quality of bamboo strips. The experimental results show that after training with the acquired image dataset, the YOLOv5s model can exert an intelligent detection effect on five common types of defects in bamboo strips, and the Ghost module makes YOLOv5s lightweight, which can effectively reduce model parameters and improve detection speed while maintaining recognition accuracy. Meanwhile, the C2f module and CA module can further leverage the model’s ability to identify specific defects in bamboo strips after lightweight improvement.

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Defective Edge Detection System Design for Long Bamboo Batten

Cited by 2 publications

References 5 publications

Improved YOLOV4-CSP Algorithm for Detection of Bamboo Surface Sliver Defects With Extreme Aspect Ratio

Improved YOLOV4-CSP Algorithm for Detection of Bamboo Surface Sliver Defects With Extreme Aspect Ratio

An Improved YOLOv5 Algorithm for Bamboo Strip Defect Detection Based on the Ghost Module

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