Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei 063210, China.
Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei 063210, China.
Comput Intell Neurosci. 2022 Mar 30;2022:2309317. doi: 10.1155/2022/2309317. eCollection 2022.
In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO particles in high temperature, a method based on the improved DeepLab 3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLab 3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.
为揭示铁尾矿中高炉渣的溶解行为，选用铁尾矿的主要成分 SiO 进行研究。针对高温下熔融 SiO 颗粒跟踪信息丢失和提取不准确的问题，提出了一种基于改进 DeepLab3+网络的方法，对小目标颗粒进行实时跟踪、分割和提取。首先，通过改进 DeepLab3+网络的解码层，构建具有不同扩张率的密集 ASPP(atrous spatial pyramid pooling)模块，优化特征提取，增加骨干网络的浅层卷积，并将其合并到上卷积解码部分，以增加细节捕获。其次，集成轻量级网络 MobileNet v3 以减少网络参数，进一步加快图像检测速度，并减少内存使用，从而实现实时图像分割并适应低配置硬件。最后，改进本文中小目标二进制分类模型的损失函数的表达，结合 Dice Loss 二进制分类分割的优点和 Focal Loss 对正负样本的平衡，解决了因正样本比例小而导致数据集不平衡的问题。实验结果表明，所提出模型的小目标分割 MIoU(平均交并比)比原始模型高 6%，整体 MIoU 提高了 3%，执行时间和内存消耗仅为原始模型的一半，可以很好地应用于小颗粒的实时跟踪和分割。
