A Lightweight YOLOv5 Optimization of Coordinate Attention

Wu, Jun; Dong, Jiaming; Nie, Wanyu; Ye, Zhiwei

doi:10.3390/app13031746

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…It is one of the advanced papers on road defect detection in recent years. The experiment was carried out under the same experimental conditions as the YOLOv5 model improved by Guo 50 and the YOLOv7 model improved by Pham V 51 . Compared to the aforementioned algorithms, YOLOv8-PD achieves the best performance in terms of mAP50, mAP50:95, and F1-Score.…”

Section: Resultsmentioning

confidence: 99%

YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model

Zeng,

Zhong

2024

Sci Rep

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Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale pavement distresses and high costs in detection task, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (pavement distress). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the large separable kernel attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road damages while reducing the computational load. Furthermore, we introduced lightweight shared convolution detection head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement distress.

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Section: Resultsmentioning

confidence: 99%

YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model

Zeng,

Zhong

2024

Sci Rep

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“…The experiment also utilized other attention mechanism modules, such as EMA attention 44 and CA attention 45…”

Section: Comparison Experiments On Attention Mechanismsmentioning

confidence: 99%

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

zeng,

Zhong

2024

Preprint

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Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement cracks.

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“…By placing the attention module before the SPPF, the refined feature representation from the attention module can be efficiently combined with the spatially pooled features. This strategic placement facilitates enhanced network performance by enabling selective focus on the most relevant features [37]. Furthermore, including ECA-Net after every C3 block in the downsampling process can improve the network's performance by augmenting its capacity to capture long-range interdependencies among distinct spatial locations within the feature map [38].…”

Section: Enhancing Object Detection With Attention Mechanismmentioning

confidence: 99%

Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management

Sami,

Sakib,

Deb

et al. 2023

Algorithms

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Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for real-time road pavement damage detection in photographic representations of outdoor road surfaces, making it an indispensable tool for efficient, real-time, and cost-effective road infrastructure management. The YOLOv5 model has been modified to incorporate several techniques that improve its accuracy and generalization performance. These techniques include the Efficient Channel Attention module (ECA-Net), label smoothing, the K-means++ algorithm, Focal Loss, and an additional prediction layer. In addition, a 1.9% improvement in mean average precision (mAP) and a 1.29% increase in F1-Score were attained by the model in comparison to YOLOv5s, with an increment of 1.1 million parameters. Moreover, a 0.11% improvement in mAP and 0.05% improvement in F1 score was achieved by the proposed model compared to YOLOv8s while having 3 million fewer parameters and 12 gigabytes fewer Giga Floating Point Operation per Second (GFlops).

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A Lightweight YOLOv5 Optimization of Coordinate Attention

Cited by 11 publications

References 29 publications

YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model

YOLOv8-PD: an improved road damage detection algorithm based on YOLOv8n model

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management

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