Improved Lightweight YOLOv8 With DSConv and Reparameterization for Continuous Casting Slab Detection on Embedded Device

doi:10.1109/TIM.2024.3509532

Improved Lightweight YOLOv8 With DSConv and Reparameterization for Continuous Casting Slab Detection on Embedded Device

2025-01-01

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DOI：10.1109/TIM.2024.3509532

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The molten steel is transformed into slabs in the process of continuous casting. These slabs are subsequently conveyed to the hot rolling area for further processing or stored in the slab storage area for future utilization. An improved lightweight object detection algorithm called You Only Look Once version 8 (YOLOv8) with DSConv, light Efficient RepGFPN, and CARAFE (DSCLERC-YOLOv8) was proposed in this article for real-time detection of slabs transmitted on the roller table, to be applied in embedded devices. DSCLERC-YOLOv8 utilizes an enhanced C2f with distribution shifting convolution (C2f_DSConv) as the feature extraction module, which enhances the processing speed of the object detection algorithm while maintaining accuracy. Moreover, the algorithm incorporates an improved Lightweight Efficient-reparameterization generalized feature pyramid network (RepGFPN) and content-aware reassembly of features (CARAFE) for feature map upsampling, enhancing the accuracy and efficiency of image prediction. In addition, the article investigates the impact of reducing network stages on the algorithm. For practical applications, a P4 network structure is employed, resulting in a slab detection accuracy rate of 86.91% for mAP50:95. Although this accuracy is slightly lower than YOLOv8n (87.78%) by 0.87%, the significant improvement of this algorithm lies in a 14.7% reduction in network latency, a 77.22% reduction in parameter count, a 66.29% reduction in computational complexity, and a 62.90% reduction in model size. Finally, by implementing model pruning, quantization, and deploying with the TensorRT engine, the algorithm successfully achieves a real-time speed of 76 FPS on Jeston Orin NX. This algorithm offers a dependable solution for the real-time detection of steel slabs during continuous casting and rolling processes in the steel industry.

Keywords:

Slabs

Feature extraction

Convolution

Accuracy

Object detection

Real-time systems

Computational modeling

Steel

Transformers

Head

Distribution shifting convolution (DSConv)

embedded devices

lightweight You Only Look Once version (YOLOv8)

object detection

reparameterization

slab detection

Improved Lightweight YOLOv8 With DSConv and Reparameterization for Continuous Casting Slab Detection on Embedded Device

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