Kou Qiqi, Ma Haohui, Xu Jinyang, Jiang He, Cheng Deqiang
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel). 2023 Jul 31;23(15):6831. doi: 10.3390/s23156831.
Foreign bodies often cause belt scratching and tearing, coal stacking, and plugging during the transportation of coal via belt conveyors. To overcome the problems of large parameters, heavy computational complexity, low classification accuracy, and poor processing speed in current classification networks, a novel network based on ESCBAM and multichannel feature fusion is proposed in this paper. Firstly, to improve the utilization rate of features and the network's ability to learn detailed information, a multi-channel feature fusion strategy was designed to fully integrate the independent feature information between each channel. Then, to reduce the computational amount while maintaining excellent feature extraction capability, an information fusion network was constructed, which adopted the depthwise separable convolution and improved residual network structure as the basic feature extraction unit. Finally, to enhance the understanding ability of image context and improve the feature performance of the network, a novel ESCBAM attention mechanism with strong generalization and portability was constructed by integrating space and channel features. The experimental results demonstrate that the proposed method has the advantages of fewer parameters, low computational complexity, high accuracy, and fast processing speed, which can effectively classify foreign bodies on the belt conveyor.
在通过带式输送机运输煤炭的过程中，异物经常会导致皮带划伤、撕裂、积煤和堵塞。为了克服当前分类网络中存在的参数大、计算复杂度高、分类准确率低以及处理速度慢等问题，本文提出了一种基于ESCBAM和多通道特征融合的新型网络。首先，为了提高特征利用率和网络学习详细信息的能力，设计了一种多通道特征融合策略，以充分整合各通道之间的独立特征信息。然后，为了在保持优异特征提取能力的同时减少计算量，构建了一个信息融合网络，该网络采用深度可分离卷积和改进的残差网络结构作为基本特征提取单元。最后，为了增强图像上下文理解能力并提高网络的特征性能，通过整合空间和通道特征构建了一种具有强泛化性和可移植性的新型ESCBAM注意力机制。实验结果表明，所提方法具有参数少、计算复杂度低、准确率高和处理速度快等优点，能够有效地对带式输送机上的异物进行分类。
