Liu Jingyi, Zhang Hanquan, Xiao Dong
School of Sciences, Northeastern University, Shenyang, 110819, China.
School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
Sci Rep. 2023 Oct 19;13(1):17880. doi: 10.1038/s41598-023-45186-0.
This paper presents an improved Fast-Segmentation Convolutional Neural Network (Fast-SCNN) and U-Net networks based on the channel attention mechanism. While ensuring the speed of network detection, the accuracy of image segmentation is also considered. The experimental results show that the accuracy rate of improved Fast-SCNN based on the channel attention mechanism is greatly improved compared with the original Fast-SCNN, reaching 88.056%, and the mean intersection over union is also improved to a certain extent, reaching 81.087%, and the detection speed is better than the original Fast-SCNN network. The accuracy of improved U-Net network based on the channel attention mechanism is 0.91805, which is better than the original U-Net network. In terms of detection speed, the improved U-Net network based on channel attention mechanism has greatly improved compared with the original U-Net network, reaching 24.02 frames per second. In addition, a method of preventing clogging of ore conveyor belts based on static image detection is proposed in this paper. By judging and predicting the blockage of the ore conveyor belt. When the conveyor belt is about to be blocked or has been blocked, the fuzzy algorithm is used to control the ore conveyor belt to slow down and stop, to improve the safety and efficiency of the conveyor belt.
本文提出了一种基于通道注意力机制的改进型快速分割卷积神经网络（Fast-SCNN）和U-Net网络。在确保网络检测速度的同时，还考虑了图像分割的准确性。实验结果表明，基于通道注意力机制的改进型Fast-SCNN的准确率与原始Fast-SCNN相比有了很大提高，达到了88.056%，平均交并比也有一定程度的提高，达到了81.087%，并且检测速度优于原始Fast-SCNN网络。基于通道注意力机制的改进型U-Net网络的准确率为0.91805，优于原始U-Net网络。在检测速度方面，基于通道注意力机制的改进型U-Net网络与原始U-Net网络相比有了很大提高，达到了每秒24.02帧。此外，本文还提出了一种基于静态图像检测的防止矿石输送带堵塞的方法。通过判断和预测矿石输送带的堵塞情况。当输送带即将堵塞或已经堵塞时，采用模糊算法控制矿石输送带减速并停止，以提高输送带的安全性和效率。
