Research on Fabric Defect Detection Technology Based on EDSR and Improved Faster RCNN

Yao, Li; Zhang, Naigang; Gao, Ao; Yan, Wei

doi:10.1007/978-3-031-10989-8_38

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Equation (7), Noise(i, j) = 1 indicates that the point is a suspected noise pixel point, while Noise(i, j) = 0 indicates that the point is a normal pixel point; δ represents the gray scale deviation, which is generally 1. In addition to the pixel points containing noise in the suspected noise pixel points, there are still some remaining normal pixel points with a gray value of 255 or 0, so it is still necessary to process the suspected noise points.…”

Section: High-speed Adaptive Median Filtering Algorithm Hsmfmentioning

confidence: 99%

“…Consequently, this algorithm is not suitable for generalization. Reference [7] proposes the combination of a Region Partitioning Network (RPN) and Faster R-CNN to form an attention mechanism, further enhancing the detection accuracy. However, the network complexity is relatively high, resulting in a GPU frame rate of only 5fps and a poor inference speed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Yu,

Lei,

Shen

et al. 2024

Electronics

View full text Add to dashboard Cite

With the development of UAV automatic cruising along power transmission lines, intelligent defect detection in aerial images has become increasingly important. In the process of target detection for aerial photography of transmission lines, insulator defects often pose challenges due to complex backgrounds, resulting in noisy images and issues such as slow detection speed, leakage, and the misidentification of small-sized targets. To address these challenges, this paper proposes an insulator defect detection algorithm called DFCG_YOLOv5, which focuses on improving both the accuracy and speed by enhancing the network structure and optimizing the loss function. Firstly, the input part is optimized, and a High-Speed Adaptive Median Filtering (HSMF) algorithm is introduced to preprocess the images captured by the UAV system, effectively reducing the noise interference in target detection. Secondly, the original Ghost backbone structure is further optimized, and the DFC attention mechanism is incorporated to strike a balance between the target detection accuracy and speed. Additionally, the original CIOU loss function is replaced with the Poly Loss, which addresses the issue of imbalanced positive and negative samples for small targets. By adjusting the parameters for different datasets, this modification effectively suppresses background positive samples and enhances the detection accuracy. To align with real-world engineering applications, the dataset utilized in this study consists of unmanned aircraft system machine patrol images from the Yunnan Power Supply Bureau Company. The experimental results demonstrate a 9.2% improvement in the algorithm accuracy and a 26.2% increase in the inference speed compared to YOLOv5s. These findings hold significant implications for the practical implementation of target detection in engineering scenarios.

show abstract

Section: High-speed Adaptive Median Filtering Algorithm Hsmfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Yu,

Lei,

Shen

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Although the traditional visual inspection method has many applications, its adaptability and accuracy are weak. [6][7][8] At present, deep learning methods are widely used in industrial inspection, but there are still few detection models suitable for ceramic tiles. Zhao Chu et al [9] proposed an improved Faster RCNN algorithm for tile surface defect detection.…”

Section: Introductionmentioning

confidence: 99%

Detection of ceramic tile defects based on improved YOLOv5

Liu

Gao²,

Xiang-dong

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

View full text Add to dashboard Cite

Aiming at the problem that the defect detection algorithm of ceramic tile is not much and the performance is not enough to meet the industrial requirements, this paper proposes a ceramic tile defect detection algorithm based on improved yolov5. The improvement scheme proposed in this paper includes the introduction of CNB module and Ghost convolution, and the improvement of spatial pyramid pooling. Through repetitive experiments on the collected tile data set, it can be confirmed that the proposed modules have obvious improvement effects on the accuracy, real-time and lightweight of the model. The mAP of the improved algorithm in this paper reaches 73.1 % under the premise of meeting the industrial demand in terms of speed and memory usage, which is 6.2 % higher than the original yolov5 model.

show abstract

Research on Fabric Defect Detection Technology Based on EDSR and Improved Faster RCNN

Cited by 2 publications

References 12 publications

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Detection of ceramic tile defects based on improved YOLOv5

Contact Info

Product

Resources

About