To detect corrugated paper quickly and accurately, a YOLOv5-based algorithm has been created. In order to improve the fusion of lower layer feature information, the operation of GSConv layer is introduced in the feature enhancement network algorithm. for better utilizing feature semantic information, the CARAFE upsampling operator is used instead of the original upsampling. MobileNetv3 was used to replace its backbone network to reduce computing costs and achieve lightweighting. Using the corrugated paper dataset constructed by the authors as an example, the improved algorithm was found to have a parameter size of 1.3 M based on the results., computational complexity of 1.8 GFLOPS, and weight size of 2.9 MB. Compared to the original YOLOv5n algorithm, these values decreased by 27.8%, 56.1%, and 21.6%. Its key technical indicators mAP@0.5 and FPS can be as high as 88.0% and 68.5 respectively. These results show that the improved algorithm satisfies the requirements for on-site deployment on mobile devices. The research shows that applying improved algorithms to conduct real-time and accurate inventory of corrugated packaging is a feasible approach.
An improved YOLOv5s model was proposed to address the issue of poor efficiency in steel pipe weld defect detection. First, the network's feature learning and localization capabilities are enhanced by introducing a coordinate attention mechanism. Secondly, compress the model through the lightweight Ghost module to effectively get fewer network parameters. Thirdly, standard convolution and depth-separable convolution are combined in the Neck module for convolution operations, aiming to reduce model complexity while maintaining accuracy. Finally, introduced a new WIoU loss function to speed up model convergence. Based on a self-created dataset, the detection of three types of surface defects, including overlap, spatter, and undercut, is conducted. The experimental findings demonstrate that the improved model achieves a 2.4% increase in mAP. Furthermore, the model also achieved a 22.4% reduction in parameter count. These findings indicate that the improved model can effectively meet the practical detection requirements. The experimental comparison confirms the feasibility and effectiveness of the proposed improved algorithm for detecting surface defects in steel pipe welds.
In order to solve the puzzles such as missed detection, poor real-time performance, low accuracy, and limited front-end hardware in PCB board surface defect detection, an improved lightweight model based on YOLOv5s is proposed. First, the C3Ghost module is used to replace the C3 module in the model backbone network, secondly the lightweight GhostConv convolution compression model is introduced, Once again ECANet is added to the backbone network to strengthen the ability to extract key information, and finally the image data set is re-clustered with the help of K-means clustering + genetic algorithm. Experimental results show the Parameters and Size of the model proposed in this article are reduced by 32.3% and 33.8% respectively, reducing the dependence on the detection front-end hardware conditions and improving the detection performance mAP@0.5 of the model. Its detection accuracy is high, the research shows that using the improved model to identify and classify PCB surface defect detection has better real-time performance and higher detection accuracy.
In order to solve the problem of low detection accuracy caused by dense placement of PCB industrial equipment and occlusion overlap in industrial AR application system, an industrial equipment object detection algorithm based on improved YOLOv7 was proposed. Firstly, Coordinate attention is added into the network to strengthen the network's attention to the visible regional features of industrial equipment. Then SIoU loss function was used to improve the model loss function, which increased the convergence speed and effectively improved the regression accuracy of object position. Finally, Adaptive-NMS is introduced to adjust the threshold adaptively according to the density of objects so as to retain more correct prediction boxes. Based on the PCB industrial equipment dataset constructed by the author, the field test results verified that the proposed algorithm would be better than other 6 advanced object detection algorithms such as Fast R-CNN, and its object detection accuracy could reach 94.21%, showing significant computational advantages for the processing of occlusion equipment. The research result shows that the industrial equipment object detection algorithm based on the improved YOLOv7 is reasonable, feasible and effective, and the proposed method is more usable.
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