A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Li, Songjiang; Wang, Shilong; Wang, Peng

doi:10.3390/s23167145

Cited by 31 publications

(4 citation statements)

References 46 publications

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“…Regarding practical deployment application, the YOLO series of algorithms have demonstrated relatively good deployment effectiveness across multiple areas [42][43][44],…”

Section: Discussionmentioning

confidence: 99%

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Yao,

Lin,

Bai

et al. 2024

Forests

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Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model’s focus on the TLB’s small targets and optimize detection accuracy. Furthermore, the model’s loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation.

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“…Regarding practical deployment application, the YOLO series of algorithms have demonstrated relatively good deployment effectiveness across multiple areas [42][43][44],…”

Section: Discussionmentioning

confidence: 99%

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

Yao,

Lin,

Bai

et al. 2024

Forests

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“…To evaluate the performance of these methods, accuracy, precision [26], recall [27], mAP@.5 [28], and mAP@.5:.95 [29] are used; equations are as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n

Lan,

Jiang,

Ding

et al. 2024

Mathematics

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With the development of the intelligent vision industry, ship detection and identification technology has gradually become a research hotspot in the field of marine insurance and port logistics. However, due to the interference of rain, haze, waves, light, and other bad weather, the robustness and effectiveness of existing detection algorithms remain a continuous challenge. For this reason, an improved YOLOv8n algorithm is proposed for the detection of ship targets under unforeseen environmental conditions. In the proposed method, the efficient multi-scale attention module (C2f_EMAM) is introduced to integrate the context information of different scales so that the convolutional neural network can generate better pixel-level attention to high-level feature maps. In addition, a fully-concatenate bi-directional feature pyramid network (Concatenate_FBiFPN) is adopted to replace the simple superposition/addition of feature map, which can better solve the problem of feature propagation and information flow in target detection. An improved spatial pyramid pooling fast structure (SPPF2+1) is also designed to emphasize low-level pooling features and reduce the pooling depth to accommodate the information characteristics of the ship. A comparison experiment was conducted between other mainstream methods and our proposed algorithm. Results showed that our proposed algorithm outperformed other models by achieving 99.4% of accuracy, 98.2% of precision, 98.5% of recall, 99.1% of mAP@.5, and 85.4% of mAP@.5:.95 on the SeaShips dataset.

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“…Meanwhile, to target TSDR in complex road conditions, YOLOv5-based improved models have been constructed by modifying the backbone, neck, head, or loss [30][31][32][33][34][35][36]. Li et al [37] improved YOLOv7 by integrating the self-attention and convolutional mix modules into an added layer for small-target detection. She et al [38] improved YOLOv7-Tiny, which utilizes the channel attention mechanism to construct a new SliceSample module, to reduce the loss of feature information.…”

Section: Introductionmentioning

confidence: 99%

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

Chen,

Dai,

et al. 2024

WEVJ

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Traffic-sign detection and recognition (TSDR) is crucial to avoiding harm to pedestrians, especially children, from intelligent connected vehicles and has become a research hotspot. However, due to motion blurring, partial occlusion, and smaller sign sizes, pedestrian TSDR faces increasingly significant challenges. To overcome these difficulties, a CTM-YOLOv8n model is proposed based on the YOLOv8n model. With the aim of extracting spatial features more efficiently and making the network faster, the C2f Faster module is constructed to replace the C2f module in the head, which applies filters to only a few input channels while leaving the remaining ones untouched. To enhance small-sign detection, a tiny-object-detection (TOD) layer is designed and added to the first C2f layer in the backbone. Meanwhile, the seventh Conv layer, eighth C2f layer, and connected detection head are deleted to reduce the quantity of model parameters. Eventually, the original CIoU is replaced by the MPDIoU, which is better for training deep models. During experiments, the dataset is augmented, which contains the choice of categories ‘w55’ and ‘w57’ in the TT100K dataset and a collection of two types of traffic signs around the schools in Tianjin. Empirical results demonstrate the efficacy of our model, showing enhancements of 5.2% in precision, 10.8% in recall, 7.0% in F1 score, and 4.8% in mAP@0.50. However, the number of parameters is reduced to 0.89M, which is only 30% of the YOLOv8n model. Furthermore, the proposed CTM-YOLOv8n model shows superior performance when tested against other advanced TSDR models.

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A Small Object Detection Algorithm for Traffic Signs Based on Improved YOLOv7

Cited by 31 publications

References 46 publications

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

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