Lightweight convolutional neural network for UAV onboard visual anomaly detection and localization
Zihan Jiang, Guiping Zhao, Xiaoqi Xu, Peilong Zhang · Scientific Reports · 2026
Unmanned aerial vehicle platforms increasingly demand efficient visual anomaly detection capabilities for infrastructure inspection and environmental monitoring applications. However, deploying sophisticated deep learning models on resource-constrained embedded processors remains challenging due to computational limitations. This paper proposes a lightweight convolutional neural network architecture specifically designed for UAV onboard anomaly detection and localization. The framework centers on a Ghost-Separable Block that integrates ghost convolution, depthwise separable convolution, and coordinate attention into a single unit tailored for aerial imagery; rather than a wholly new operator, it is a deliberate combination whose value we establish empirically, trimming parameters by 26% while preserving feature discrimination. The embedded coordinate attention preserves directional spatial information essential for accurate target localization in aerial imagery. The bidirectional feature pyramid network enables effective multi-scale fusion to handle scale variations inherent in UAV-captured scenes. A dual-branch prediction head with Complete Intersection over Union loss performs simultaneous anomaly classification and spatial regression. Extensive experiments on a collected UAV aerial anomaly dataset demonstrate that the proposed method achieves 81.2% mean average precision at 0.5 IoU threshold with only 2.18 million parameters, surpassing mainstream lightweight detectors such as YOLOv8n and EfficientDet-D0: it raises mean average precision while cutting parameters by 31% and computation by 59% relative to YOLOv8n. The dual-branch head and the Complete Intersection over Union loss are adopted rather than newly proposed; the block arrangement is where the contribution lies. Beyond the self-collected data, the model preserves its accuracy-efficiency advantage on the public VisDrone benchmark, and on a Jetson Xavier NX it sustains 42 FPS natively and 67 FPS after TensorRT FP16 optimization within a 15 W power envelope, confirming real-time onboard feasibility.