LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

Zhao, Huajun; Wan, Fang; Lei, Guangbo; Xiong, Ying; Xu, Li; Xu, Chong‐Yu; Wen, Zhou

doi:10.3390/s23146558

Cited by 17 publications

(9 citation statements)

References 41 publications

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“…(3) In the Yolov8+ model, the Neck component incorporates a BiFPN fusion architecture and spatial attention mechanisms to overcome the challenge of inadequate long-distance information propagation in object detection. This design enhancement boosts the model's capability [16] to detect small and obscured targets, showcasing superior performance in real-world scenarios. Additionally, the Head component was expanded to produce a 10 × 10 output.…”

Section: Contribution Of This Articlementioning

confidence: 97%

“…The IOU loss function in YOLOv8 is the same as that in YOLOv5, and its calculation formula is shown in Equation (16), where IOU represents the intersection over union, b and b gt represent the centroids of two rectangular boxes, p represents the Euclidean distance between the two rectangular boxes, c represents the diagonal distance of the enclosed regions of the two rectangular boxes, v is used to measure the consistency of the relative proportions of the two rectangular boxes, and a is the weighting coefficient [35].…”

Section: Fusion Loss Functionmentioning

confidence: 99%

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Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

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The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

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Section: Contribution Of This Articlementioning

confidence: 97%

Section: Fusion Loss Functionmentioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…This can not only enrich the background of the data set, improve the robustness of the system, but also reduce the loss of GPU memory and accelerate the training speed of the network. Compared with the previous generation, adaptive anchor frame calculation is added [24]. During training, the best anchor frame value of different training sets can be calculated adaptively.…”

Section: Improved Yolov5 Algorithmmentioning

confidence: 99%

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

Li,

et al. 2024

Sensors

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The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

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“…The experimental results show that on the Northeastern University Defect Detection (NEU-DET) dataset, the improved YOLOv7 network model improves the mean average precision (mAP) by 6% compared to that of the original algorithm. Zhao et al 33 developed the stem module and combined it with the MobileNetv2 module with a CA attention mechanism and integrated it into the backbone network of YOLOv5 to decrease the number of model parameters and increase the detection speed. The speed of detecting objects with the LSD-YOLOv5 model increased by 28.7%.…”

Section: Related Workmentioning

confidence: 99%

Lightweight strip steel defect detection algorithm based on improved YOLOv7

Lu,

Yu,

Liu

2024

Sci Rep

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The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

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LSD-YOLOv5: A Steel Strip Surface Defect Detection Algorithm Based on Lightweight Network and Enhanced Feature Fusion Mode

Cited by 17 publications

References 41 publications

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

Lightweight strip steel defect detection algorithm based on improved YOLOv7

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