PFFNET: A Fast Progressive Feature Fusion Network for Detecting Drones in Infrared Images

Abstract

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%.

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

“…The disadvantage of electromagnetic interference or reflection is that its detection range is limited by signal strength and frequency, and it is vulnerable to encryption or spoofing [8]. The disadvantage of infrared is that its detection is affected by ambient temperature and humidity, and it requires complex temperature calibration and analysis [9]. Therefore, optical image-based detection methods can be used, and UAV detection methods using image detection have many advantages that make them highly favored in various application scenarios.…”