Kangnan Quan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Kangnan Quan has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Cong Zhang
Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China
1 shared publication - Mingkai Yue
School of Equipment Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication - Zhen Heng Feng
Science and Technology on Electromechanical Dynamic Control Laboratory, Beijing Institute of Technology, Beijing 100081, China
1 shared publication - Ziqiang Han
School of Equipment Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication
Latest Publications
- Open Access
PFFNET: A Fast Progressive Feature Fusion Network for Detecting Drones in Infrared Images
The rampant misuse of drones poses a serious threat to national security and human life. Currently, CNN (Convolutional Neural Networks) are widely used to detect drones. However, small drone targets often reduced amplitude or even lost features in infrared images which traditional CNN cannot overcome. This paper proposes a Progressive Feature Fusion Network (PFFNET) and designs a Pooling Pyramid Fusion (PFM) to provide more effective global contextual priors for the highest downsampling output. Then, the Feature Selection Model (FSM) is designed to improve the use of the output coding graph and enhance the feature representation of the target in the network. Finally, a lightweight segmentation head is designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm has good real-time performance and high accuracy in drone target detection. On the public dataset, the intersection over union (IOU) is improved by 2.5% and the detection time is reduced by 81%.
Open AccessPFFNET: A Fast Progressive Feature Fusion Network for Detecting Drones in Infrared Images
The rampant misuse of drones poses a serious threat to national security and human life. Currently, CNN (Convolutional Neural Networks) are widely used to detect drones. However, small drone targets often reduced amplitude or even lost features in infrared images which traditional CNN cannot overcome. This paper proposes a Progressive Feature Fusion Network (PFFNET) and designs a Pooling Pyramid Fusion (PFM) to provide more effective global contextual priors for the highest downsampling output. Then, the Feature Selection Model (FSM) is designed to improve the use of the output coding graph and enhance the feature representation of the target in the network. Finally, a lightweight segmentation head is designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm has good real-time performance and high accuracy in drone target detection. On the public dataset, the intersection over union (IOU) is improved by 2.5% and the detection time is reduced by 81%.