YOLOv8-FDD: A Real-Time Vehicle Detection Method Based on Improved YOLOv8

Liu, Xiaojia; Wang, Yipeng; Yu, Dexin; Yuan, Zimin

doi:10.1109/access.2024.3453298

IEEE Access

2024

DOI: 10.1109/access.2024.3453298

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YOLOv8-FDD: A Real-Time Vehicle Detection Method Based on Improved YOLOv8

Xiaojia Liu,

Yipeng Wang,

Dexin Yu

et al.

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2024

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Cited by 3 publications

References 27 publications

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Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Wang,

Li,

Deng

et al. 2024

Agriculture

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Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

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Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Wang,

Li,

Deng

et al. 2024

Agriculture

View full text Add to dashboard Cite

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Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition

Lin,

Chen,

Lin

et al. 2024

Mathematics

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Convolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. However, conventional pooling methods often lead to a loss of critical feature information, particularly in images with diverse content, such as vehicle images. This study proposes a novel approach to address these problems: a convolutional neural network with type-2 fuzzy-based pooling (CNN-T2FP). This innovative pooling method utilizes type-2 fuzzy membership functions to effectively manage local imprecision in feature maps. Compared with type-1 fuzzy pooling, which only addresses uncertainty to a certain extent, type-2 fuzzy pooling exhibits improved adaptability to different image contents. The experimental results of this study revealed that the CNN-T2FP achieved average accuracies of 92.14% and 93.34% on two datasets, surpassing the performance of existing pooling techniques. In addition, t-distributed stochastic neighbor embedding plots and feature visualization maps further highlighted the potential of type-2 fuzzy-based pooling to overcome the limitations of conventional pooling methods and enhance the performance of convolutional neural networks in image analysis tasks.

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YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

Zhao,

Fu,

Jia

et al. 2024

Sensors

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In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features. Second, we incorporate Omni-directional Dynamic Convolution (ODConv) into the head network, which is capable of simultaneously learning complementary attention across the four dimensions of the kernel space, therefore facilitating the capture of multifaceted features from the input data. Lastly, we introduce Shape-IOU, a new loss function that significantly enhances the accuracy and robustness of detection results for vehicles of varying sizes. Experimental evaluations conducted on the UA-DETRAC dataset demonstrate that our model achieves improvements of 4.7 and 4.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, compared to the baseline model. Furthermore, comparative experiments on the SODA10M dataset corroborate the superiority of our method in terms of precision and accuracy.

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YOLOv8-FDD: A Real-Time Vehicle Detection Method Based on Improved YOLOv8

Cited by 3 publications

References 27 publications

Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition

YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

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