In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset. 

/pdf/strip-surface-defect-detection-algorithm-based-on-yolov5-21z4aft3.pdf

Strip Surface Defect Detection Algorithm Based on YOLOv5

Filter pruning by quantifying feature similarity and entropy of feature maps

Pruning Deep Neural Networks for Green Energy-Efficient Models: A Survey

Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting remarkable achievements. Nevertheless, their substantial workload and inherent redundancies pose ongoing challenges for both practitioners and academia. While numerous researchers endeavor to optimize DNNs, the inherent parallelism features of hardware are generally underutilized, resulting in inefficient use of hardware resources. To address this deficit, the paper unveils a hardware-aware mechanism, IHSOpti, which incorporates hardware characteristics with software algorithms for DNN optimization. IHSOpti endeavors to exploit the full potential of modern hardware parallelism, with significant emphasis on pipelining mechanisms. Specifically, IHSOpti formulates an advanced sparse training algorithm Polar_HSPG which incorporates the newly-proposed layer-wise refined polarization regularizer (LWPolar), grounded on the half-space project gradient (HSPG). Subsequently, IHSOpti pioneeringly introduces the residual strategy for optimizing the layer-level redundancies of neural networks, capitalizing on the pipelining attributes inherent in current hardware. Experimental findings demonstrate that IHSOpti attains outstanding pruning ratios in both parameters and FLOPs. Specifically, IHSOpti achieves up to 96.90% and 82.73% pruning ratios with the accuracy of 93.34% for VGGBN, 97.69% and 95.24% pruning ratios with the accuracy of 94.69% for ResNet, 98.07% and 97.80% pruning ratios with the accuracy of 95.73% for the cutting-edge network RegNet, respectively. Notably, the running efficiency exhibits remarkable improvements with accelerations ranging from 3.63× to 8.20× for CPUs and 1.22× to 2.25× for GPUs, respectively. These outcomes surpass the latest advances in the field. Through the incorporation of specific hardware characteristics, IHSOpti provides a comprehensive and effective approach to harness the intrinsic parallelism of contemporary hardware platforms for DNNs. 

Hardware-aware approach to deep neural network optimization

Deep neural network models, especially CNNs, have a wide range of applications in many fields, but their high computational power requirements limit the deployment applications in many resource-constrained embedded devices. Pruning techniques reduce the computational power requirements of models by removing redundant structures from CNNs. Most existing static pruning methods use a global uniform pruning rate to prune pre-trained models and require finetuning to recover accuracy after pruning, resulting in high training costs, and the global uniform pruning rate is sub-optimal. While dynamic pruning methods perform pruning during training and use auxiliary modules to calculate the saliency scores of channels, but do not exploit its function of assisting network training. We propose an adaptive structured continuous sparse network pruning method based on the attention mechanism that prunes the original network during training. The attention-based channel similarity calculation module calculates the channel saliency scores while refining features to assist network training, and the adaptive continuous sparse control module gradually discretizes the channel saliency scores and assigns the pruning rate of each layer according to the preset pruning rate target. The pruned model is output after training and no additional fine-tuning is required. We validate the proposed method on CIFAR-10 and the large-scale dataset Imagenet using networks with different structures, and our method outperforms the comparative pruning methods at different pruning rates. In CIFAR-10, our method can reduce VGG-16 by 34.4% FLOPs while top-1 accuracy increased by 0.19%. In Imagenet, we can reduce ResNet-34 by 51.5% FLOPs, while the top-1 accuracy decreases by only 0.89%. 

Attention-based adaptive structured continuous sparse network pruning

FPFS: Filter-level pruning via distance weight measuring filter similarity

We studied laser-induced liquid indentations generated by the Marangoni effect. We showed experimental results along with the simulation model based on the lubrication theory.

Laser-Induced Liquid Deformation Driven by the Marangoni Effect

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FPFS: Filter-level pruning via distance weight measuring filter similarity

Laser-Induced Liquid Deformation Driven by the Marangoni Effect