Pyramid-attention based multi-scale feature fusion network for multispectral pan-sharpening

2021 ◽  
Author(s):  
Yang Chi ◽  
Jinjiang Li ◽  
Hui Fan
Keyword(s):  
Feature Fusion ◽  
Scale Feature ◽  
Multi Scale

scholarly journals MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1426
Author(s):  
Chuanyang Liu ◽  
Yiquan Wu ◽  
Jingjing Liu ◽  
Jiaming Han
Keyword(s):  
Feature Detection ◽  
Feature Fusion ◽  
Memory Storage ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Composite Insulator ◽  
Running Time ◽  
Scale Feature ◽  
Multi Scale ◽  
Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

Incorporating the Hybrid Deformable Model for Improving the Performance of Abdominal CT Segmentation via Multi-Scale Feature Fusion Network

2021 ◽  
pp. 102156
Author(s):  
Xiaokun Liang ◽  
Na Li ◽  
Zhicheng Zhang ◽  
Jing Xiong ◽  
Shoujun Zhou ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Deformable Model ◽  
Abdominal Ct ◽  
Scale Feature ◽  
Multi Scale ◽  
Ct Segmentation

scholarly journals MLFNet-Point Cloud Semantic Segmentation Convolution Network Based on Multi-scale Feature Fusion

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jingfang Yang ◽  
Bochang Zou ◽  
Huadong Qiu ◽  
Zhi Li
Keyword(s):  
Point Cloud ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Scale Feature ◽  
Multi Scale
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