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

Yu, Chengshui; Liu, Yue; Cao, Yuan; Sun, Yongkui; Su, Shuai; Yang, Weifeng; Wang, Wenkun

doi:10.1088/1361-6501/ad3a05

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…The regression loss function of the bounding box is a critical aspect in object detection. In the initial iterations of the YOLO series, the Generalized IoU Loss was employed as the loss function [34,35]. The calculation formula for GIoU is represented by Formula (14).…”

Section: Loss Function Optimizationmentioning

confidence: 99%

“…In order to evaluate the impact of YOLOv8-ASFF on detecting tea leaf blight, tea white spot disease, and tea sooty disease in Yunnan large-leaf tea, four sets of comparative experiments were conducted. The experiments compared YOLOv8-ASFF with four established mainstream network models, including YOLOv8 [35], YOLOv5 [36], CornerNet [37], and SSD [38]. To ensure the reliability of the model test results, the hardware equipment and software environment were kept consistent throughout the study.…”

Section: Dataset Training Of Yolov8mentioning

confidence: 99%

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Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

Ye,

Shao,

Yang

et al. 2024

Plants

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In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.

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Section: Loss Function Optimizationmentioning

confidence: 99%

Section: Dataset Training Of Yolov8mentioning

confidence: 99%

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

Ye,

Shao,

Yang

et al. 2024

Plants

View full text Add to dashboard Cite

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Machine learning-enhanced vision systems for cutting tool notch detection in new energy battery manufacturing

Zheng,

Wang,

Chen

et al. 2024

Meas. Sci. Technol.

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This paper presents a study on the problem of burrs on the electrodes of new energy batteries, which are a major factor contributing to battery short-circuits and explosions. During the process of electrode cutting, the use of cutting tools with a notch is likely to cause burrs on the electrode. Therefore, it is essential to accurately detect the notch of the cutting tool. This paper explores the issue of cutting tool notch detection using machine learning-enhanced vision systems. Firstly, a set of cutting tool image acquisition devices is used to capture high-quality images of the cutting tool edge. Next, an algorithm for removing attachments based on concave point matching is proposed, effectively eliminating edge attachments by analyzing the concave point information of the edge. Additionally, we propose an enhanced Zernike moment sub-pixel edge extraction method, which achieves sub-pixel edge extraction while preserving the edge characteristics of the cutting tool. Furthermore, a notch detection algorithm based on quartic Hermite interpolation is introduced to detect the notch of the cutting tool by initially identifying the tool's edge. The proposed algorithms are compared with other state-of-the-art methods, demonstrating faster and more accurate extraction of sub-pixel cutting tool edges and detection of cutting tool notches.

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A lightweight road traffic sign detection algorithm based on adaptive sparse channel pruning

Zheng,

Guan,

Chen

et al. 2024

Meas. Sci. Technol.

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The development of Traffic Sign Recognition (TSR) has become increasingly important for enhancing the safety and convenience of assisted driving. To achieve high accuracy, faster inference speed, and a lightweight model, an improved lightweight Traffic Sign Recognition network, termed YOLOv8-ALWP, has been proposed. This network incorporates Adaptive Downsampling (ADown) to replace the original convolution module in YOLOv8. By employing multiple pooling and convolution operations, it reduces the spatial resolution to extract additional feature information. To accommodate the varying scale characteristics of different traffic signs, Large Separable Kernel Attention (LSKA) is introduced to enhance Spatial Pyramid Pooling-Fast (SPPF). Furthermore, the Complete Intersection over Union (CIoU) loss has been improved, and a new Wise-Focaler-EIoU Loss has been proposed to accelerate model convergence and enhance generalization capabilities. Finally, Layer-Adaptive Sparsity for Magnitude-Based Pruning (LAMP) is employed to reduce the model's parameters, decrease computational complexity, and improve inference speed. Experiments were conducted using the TT100K, Roadsign, CCTSDB, and GTSDB datasets. In the TT100K dataset, compared to the baseline model, the improved algorithm significantly reduced parameters by 64.67%, FLOPs by 44.44%, and increased mAP by 1.7%, precision by 5.5%, and FPS from 70.3 to 81.7, respectively. Under four specific conditions, the improved algorithm effectively addressed the shortcomings of the baseline model, such as missed detections and reduced accuracy. These experimental results indicate that the YOLOv8-ALWP algorithm achieves model lightweighting while enhancing detection accuracy.

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

Cited by 4 publications

References 42 publications

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

Machine learning-enhanced vision systems for cutting tool notch detection in new energy battery manufacturing

A lightweight road traffic sign detection algorithm based on adaptive sparse channel pruning

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