License Plate Recognition System Based on Improved YOLOv5 and GRU

Shi, Hengliang; Zhao, Dongnan

doi:10.1109/access.2023.3240439

Cited by 52 publications

(15 citation statements)

References 48 publications

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“…The recognition accuracy was 97.2%, 96.2%, and 97.9%, respectively. 25 In addition, the latest version of YOLOv5 integrates the feature enhancement module (FEM) and spatial attention module (SAM) into the network, 21,26 and changes the previous detection layer to enhance small target detection. In the vehicle detection case based on YOLOv5, 26 the detection accuracy rate reached 89.8%.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…Compared with the traditional depth model, the evaluation indexes of YOLOv5 model are improved, 16,23 AP50 is increased by 18.1%, 24 the experimental results show that the dense target identification is more accurate. The recognition accuracy was 97.2%, 96.2%, and 97.9%, respectively 25 . In addition, the latest version of YOLOv5 integrates the feature enhancement module (FEM) and spatial attention module (SAM) into the network, 21,26 and changes the previous detection layer to enhance small target detection.…”

Section: Related Workmentioning

confidence: 99%

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An image augmentation‐based ice monitoring method for safe navigation of polar ships

Liang,

Wang,

Zhao

et al. 2024

Quality & Reliability Eng

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The navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An image augmentation‐based ice monitoring method for safe navigation of polar ships

Liang,

Wang,

Zhao

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…On Tesla V100, YOLOv5 achieves real-time detection speeds of 156 FPS on the COCO2017 dataset with an accuracy of 56.8% AP. In recent years, YOLOv5 has been widely applied in various fields such as industry [30,31], agriculture [32,33], etc. The structure of YOLOv5 mainly consists of four parts.…”

Section: Yolov5mentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency.

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“…On the Tesla V100, the real-time detection speed of the COCO2017 dataset reaches 156 FPS, and the accuracy rate is 56.8% AP. At present, YOLOV5 is widely used in many different application scenarios, such as agriculture [ 21 , 22 ], industry [ 23 , 24 ] and other industries. In this paper, YOLOV5s is selected as the basic algorithm, taking into account the balance between the target detection accuracy and speed.…”

Section: Related Workmentioning

confidence: 99%

Strip Surface Defect Detection Algorithm Based on YOLOv5

et al. 2023

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In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.

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License Plate Recognition System Based on Improved YOLOv5 and GRU

Cited by 52 publications

References 48 publications

An image augmentation‐based ice monitoring method for safe navigation of polar ships

An image augmentation‐based ice monitoring method for safe navigation of polar ships

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Strip Surface Defect Detection Algorithm Based on YOLOv5

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