Dynamic Video Image Segmentation Based on Dual Channel Convolutional Kernel and Multi-Frame Feature Fusion

Frontiers in Neurorobotics

Zuguo Chen¹  Ming Lu²  Chaoyang Chen² 

[1] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China;School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China;Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;

关键词: dynamic video image segmentation;    dual channel convolution kernel;    multi-frame feature fusion;    fire hole;    aluminum electrolysis cell;   

DOI  :  10.3389/fnbot.2022.845858

来源: DOAJ

【 摘 要 】

The color image of the fire hole is key for the working condition identification of the aluminum electrolysis cell (AEC). However, the image of the fire hole is difficult for image segmentation due to the nonuniform distributed illuminated background and oblique beam radiation. Thus, a joint dual channel convolution kernel (DCCK) and multi-frame feature fusion (MFF) method is developed to achieve dynamic fire hole video image segmentation. Considering the invalid or extra texture disturbances in the edge feature images, the DCCK is used to select the effective edge features. Since the obtained edge features of the fire hole are not completely closed, the MFF algorithm is further applied to complement the missing portion of the edge. This method can assist to obtain the complete fire hole image of the AEC. The experiment results demonstrate that the proposed method has higher precision, recall rate, and lower boundary redundancy rate with well segmented image edge for the aid of working condition identification of the AEC.

【 授权许可】

Unknown