CIFI-YOLO: A SWaP-Aware Object Detector for UAV Optical Sensors via Cross-Iterative Fusion and Gated Attention

Abstract

Operating optical sensors on uncrewed aerial vehicles (UAVs) requires balancing high precision and severe Size Weight and Power constraints of edge hardware. Traditional detection algorithms often fail in high altitudes because small targets hide in complex ground clutter and sensor noise. CIFI-YOLO is presented as a hardware-compatible redesign optimized for edge sensing. Three innovations for aerial sensors are introduced within the YOLOv8-based framework. Environmental interference is suppressed by a gated spatio-channel attention module (GSCA-M). High-frequency spatial details lost during deep feature extraction are recovered through a cross-iterative fusion (CIF) scheme. Multiscale context aggregation is provided by an information enhancement module (IEM). Consistent performance gains are demonstrated through evaluations on the VisDrone2019, UAV-DT, and CODrone datasets. Compared with YOLOv8s, absolute AP50 gains of 6.1%, 5.5%, and 7.7% points are achieved by CIFI-YOLO on VisDrone2019, UAV-DT, and CODrone, respectively. The parameter count is reduced by 65.0%, and SRAM memory bottlenecks are alleviated by CIFI-YOLO. A latency of 9.08 ms is achieved on the RTX 3080 Ti evaluation platform, corresponding to approximately 110 frames/s and indicating the potential for real-time edge deployment.