MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang, Xianli; Ren, Zhijie; Wan, Dahang; Zhang, Yuzhong; Shu, Shuangbao

doi:10.3390/s22249897

Cited by 21 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To balance the model size, inference speed, and detection performance, Mo-bileNetV3 was chosen as the feature extraction network in this paper. Similar to previous research [44][45][46], using MobileNetV3 as the feature extraction network had achieved a better detection performance.…”

Section: Lightweight Convolutional Neural Networksupporting

confidence: 80%

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

Zhang

Zhao

et al. 2023

Agronomy

View full text Add to dashboard Cite

Accurate and rapid detection of tea shoots within the tea canopy is essential for achieving the automatic picking of famous tea. The current detection models suffer from two main issues: low inference speed and difficulty in deployment on movable platforms, which constrain the development of intelligent tea picking equipment. Furthermore, the detection of tea canopy shoots is currently limited to natural daylight conditions, with no reported studies on detecting tea shoots under artificial light during the nighttime. Developing an all-day tea picking platform would significantly improve the efficiency of tea picking. In view of these problems, the research objective was to propose an all-day lightweight detection model for tea canopy shoots (TS-YOLO) based on YOLOv4. Firstly, image datasets of tea canopy shoots sample were collected under low light (6:30–7:30 and 18:30–19:30), medium light (8:00–9:00 and 17:00–18:00), high light (11:00–15:00), and artificial light at night. Then, the feature extraction network of YOLOv4 and the standard convolution of the entire network were replaced with the lightweight neural network MobilenetV3 and the depth-wise separable convolution. Finally, to compensate for the lack of feature extraction ability in the lightweight neural network, a deformable convolutional layer and coordinate attention modules were added to the network. The results showed that the improved model size was 11.78 M, 18.30% of that of YOLOv4, and the detection speed was improved by 11.68 FPS. The detection accuracy, recall, and AP of tea canopy shoots under different light conditions were 85.35%, 78.42%, and 82.12%, respectively, which were 1.08%, 12.52%, and 8.20% higher than MobileNetV3-YOLOv4, respectively. The developed lightweight model could effectively and rapidly detect tea canopy shoots under all-day light conditions, which provides the potential to develop an all-day intelligent tea picking platform.

show abstract

Section: Lightweight Convolutional Neural Networksupporting

confidence: 80%

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

Zhang

Zhao

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…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. Lang et al [26] introduced the MobileNetV3 module as the backbone of YOLOv5 and replaced the SPPF module with the SE attention module to reduce the number of parameters and computational complexity, thus accelerating the speed in the detection of surface defects on magnetic rings. Wu et al [27] used Ghost Conv and Ghost Bottleneck to replace the traditional convolution and bottleneck CSP module in the backbone network of YOLOv5, to reduce the number of model parameters.…”

Section: Related Workmentioning

confidence: 99%

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

Yan,

Zhang,

Bai

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…The single-stage algorithms are best known as you only look once (YOLO) ( Redmon et al., 2016 ), which turns the detection task into a simple regression problem and has a simple network model that is easy for researchers to learn and train. Although there have been many iterations of YOLO, YOLOV5 remains the most widely used version across all domains ( Lang et al., 2022 ). For example, Chen et al.…”

Section: Introductionmentioning

confidence: 99%

“…The single-stage algorithms are best known as you only look once (YOLO) (Redmon et al, 2016), which turns the detection task into a simple regression problem and has a simple network model that is easy for researchers to learn and train. Although there have been many iterations of YOLO, YOLOV5 remains the most widely used version across all domains (Lang et al, 2022). For example, Chen et al (2022) added the SE module to YOLOV5 and replaced the original loss function GIOU with EIOU to automatically identify diseases on rubber trees, finally the average accuracy was improved by 5.4% compared to the original YOLOV5.…”

Section: Introductionmentioning

confidence: 99%

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection

Lv,

2024

Front. Plant Sci.

View full text Add to dashboard Cite

Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.

show abstract

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Cited by 21 publications

References 37 publications

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

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

YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection

Contact Info

Product

Resources

About