Research on Traffic Sign Detection Based on Improved YOLOv8

Huang, Zhongjie; Li, Lintao; Krizek, Gerd Christian; Sun, Linhao

doi:10.4236/jcc.2023.117014

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…To evaluate the performance of the YOLOv8-NL model, we used a standard linear evaluation scheme [9][10][11][12][13] . Table 1 presents the evaluation results of our method compared to the baseline method.…”

Section: Resultsmentioning

confidence: 99%

“…The Wasserstein distance loss cannot adapt to multiscale types of targets and thus lacks the ability to adjust dynamically. Huang et al 13 proposed an improved YOLOv8 model, which enables the network to capture fine-grained features by means of data augmentation and employs a progressive feature pyramid network to deal with interactions between non-adjacent layers. Noise and blurring in the process of data augmentation processing will lead to the missing of some target features, while the Asymptotic Feature Pyramid Network (AFPN) will affect the speed of detection to some extent.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

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Normalization attention and lightweight convolution-based network for traffic sign detection

Zhao

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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Object detection and image recognition are considered to be the most critical and challenging aspects of computer vision. The rapid advancement of driverless technology and assisted driving systems further emphasizes the importance of traffic sign detection and recognition. Detecting small objects against complex backgrounds in practical assisted driving tasks remains a formidable challenge. Many existing target detection methods struggle to strike a balance between performance and parameters. In response, we propose a novel traffic sign detection model based on YOLOv8s-A Traffic Sign Detection Network incorporating Normalization-based Attention and Lightweight Convolution (YOLOv8-NL). The objective is to optimize the performance and address the challenge of balancing model parameters in traffic sign recognition within the transportation domain. Firstly, Normalization-based Attention (NAM) is introduced to address issues related to inconsistent input dimensions, uneven target distribution, and complex background of optimization. Secondly, lightweight Global Sparse Convolution (GSConv) is integrated to reduce the model parameters and enhance model generalization performance. Finally, to further enhance detection accuracy, the Wise-IOUv3 loss function is introduced to tackle difficulties associated with low-quality labeling of data. The effectiveness of the novel YOLOv8 model, which integrates Normalization-based Attention and Lightweight Convolution, is demonstrated through experiments conducted on two publicly available datasets. These experiments showcase the model's ability to significantly reduce parameters without compromising its guaranteed performance. It is noteworthy to note that significant improvements were observed in the CCTSDB2021 dataset, with a reduction of 1.2 million model parameters, resulting in 1% increase in mean average precision (mAP) compared to the YOLOv8s model. Furthermore, the mAP for the three-class target detection task on the TT00K dataset reached an impressive 94.2%.

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Section: Resultsmentioning

confidence: 99%

Section: Traffic Sign Detectionmentioning

confidence: 99%

Normalization attention and lightweight convolution-based network for traffic sign detection

Zhao

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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“…Thirdly, a Feature Enhancement Module (FEM) is added to highlight target details, prevent the loss of valuable features, and enhance detection accuracy. The improved YOLOv8 network [16] incorporates the characteristics of small target objects in real-world scenarios by introducing blur and noise operations. Subsequently, the network introduces the Asymptotic Feature Pyramid Network (AFPN) [17] to highlight the impact of key layer features after feature fusion, addressing direct interaction issues between non-adjacent layers.UAV-YOLOv8 [18] has optimized YOLOv8 in several aspects.…”

Section: Related Workmentioning

confidence: 99%

DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

Ren,

Song,

et al. 2024

Preprint

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The detection of lesions in various diseases remains a challenging task in medical image processing, given the diverse morphologies, sizes, and boundaries of lesions associated with different illnesses. In this paper, we propose an advanced lesion detection model named DHC-YOLO, which integrates Multi-Scale Dilated attention (MSDA) and multi-head self-attention (MHSA) within the YOLOv8 network. The method also introduces an enhanced feature fusion through the Concatenation (Concat) operation in the Feature Pyramid Networks (FPN) structure of YOLOv8. The DHC-YOLO model achieves superior performance in lesion detection by effectively aggregating semantic information across various scales in the attended receptive field, reducing redundancy in self-attention mechanisms without the need for complex operations or additional computational costs. The incorporation of MHSA enhances the network’s ability to extract diverse features, and the Concat operation in FPN improves multi-scale feature fusion. Our evaluations on brain tumor, colonic polyp, and esophageal cancer datasets demonstrate the superiority of our method over baseline YOLOv8 and several state-of-the-art object detection models. Specifically, on the brain tumor dataset, DHC-YOLO achieves mAP50 and mAP50:95 scores of 88.3% and 73.5%, respectively; on the colonic polyp dataset, the scores are 88.8% and 67.2%; and on the esophageal cancer dataset, the scores are 51.3% and 20.7%. These compelling results underscore the robust performance of DHC-YOLO in lesion detection tasks.

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“…However, this structure is relatively simple, limited to the simple addition of feature information between adjacent levels, and there still exists a semantic gap between non-adjacent levels. To address this issue, this study introduces AFPN (Asymptotic Feature Pyramid Network) [29] to enhance the interaction capability between non-adjacent levels. The two pyramid structures are illustrated in Figure 11.…”

Section: Neck Network Optimizationmentioning

confidence: 99%

Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model

Zhao,

Zhang,

Liu

et al. 2024

Applied Sciences

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In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the evaluation of rice seedling planting conditions. Therefore, in order to build a seedling insertion condition detection system, this study proposes an approach based on the combination of image processing and deep learning. The image processing stage is primarily applied to seedling absence detection, utilizing the centroid detection method to obtain precise coordinates of missing seedlings with an accuracy of 93.7%. In the target recognition stage, an improved YOLOv8 Nano network model is introduced, leveraging deep learning algorithms to detect qualified and misplaced seedlings. This model incorporates ASPP (atrous spatial pyramid pooling) to enhance the network’s multiscale feature extraction capabilities, integrates SimAM (Simple, Parameter-free Attention Module) to improve the model’s ability to extract detailed seedling features, and introduces AFPN (Asymptotic Feature Pyramid Network) to facilitate direct interaction between non-adjacent hierarchical levels, thereby enhancing feature fusion efficiency. Experimental results demonstrate that the enhanced YOLOv8n model achieves precision (P), recall (R), and mean average precision (mAP) of 95.5%, 92.7%, and 95.2%, respectively. Compared to the original YOLOv8n model, the enhanced model shows improvements of 3.6%, 0.9%, and 1.7% in P, R, and mAP, respectively. This research provides data support for the efficiency and quality of transplanting machine operations, contributing to the further development and application of unmanned field management in subsequent rice seedling cultivation.

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Research on Traffic Sign Detection Based on Improved YOLOv8

Cited by 19 publications

References 7 publications

Normalization attention and lightweight convolution-based network for traffic sign detection

Normalization attention and lightweight convolution-based network for traffic sign detection

DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model

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